Using communicative discourse trees to detect a request for an explanation

ABSTRACT

Systems, devices, and methods of the present invention relate to detecting a request for explanation in text. In an example, a method creates a discourse tree from a subset of text. The discourse tree includes nodes, each nonterminal node representing a rhetorical relationship between two of the fragments and each terminal node of the nodes of the discourse tree is associated with one of the fragments. The method forms a communicative discourse tree from the discourse tree by matching each fragment that has a verb to a verb signature. The method further identifies that the subset of text comprises a request for an explanation by applying a classification model trained to detect a request for an explanation to the communicative discourse tree.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. Ser. No. 16/260,930, filedJan. 29, 2019, which claims priority from provisional application62/624,001, filed Jan. 30, 2018, and provisional application 62/646,711,filed Mar. 22, 2018, both of which are herein incorporated by referencein their entirety.

TECHNICAL FIELD

This disclosure is generally concerned with linguistics. Morespecifically, this disclosure relates to using communicative discoursetrees to detect a request for an explanation.

BACKGROUND

Linguistics is the scientific study of language. One aspect oflinguistics is the application of computer science to human naturallanguages such as English. Due to the greatly increased speed ofprocessors and capacity of memory, computer applications of linguisticsare on the rise. For example, computer-enabled analysis of languagediscourse facilitates numerous applications such as automated agentsthat can answer questions from users. The use of autonomous agents(“chatbots”) to answer questions, facilitate discussion, managedialogues, and provide social promotion is increasingly popular. Toaddress this need, a broad range of technologies including compositionalsemantics has been developed. Such technologies can support automatedagents in the case of simple, short queries and replies.

But current solutions for autonomous agents are unable to determine whenan utterance received from a user device includes a request for anexplanation. A request for an explanation can be helpful, for example,if an autonomous agent generates a response or takes a decision based onmachine learning, which is often not transparent about how a particulardecision was reached. When a response by an autonomous agent isunsatisfactory, an explanation may be necessary to help understand thereasoning behind a response or a decision, such as why the user wasdenied a loan.

Hence, new solutions are needed.

BRIEF SUMMARY

Generally, systems, devices, and methods of the present invention arerelated to communicative discourse trees. In an example, a methodrepresents text as a communicative discourse tree and uses machinelearning to determine whether the text includes a request for anexplanation. Based on the text including a request for an explanation,the method can cause a suitable explanation to be generated andprovided.

In an aspect, a method accesses text that includes fragments. The methodcreates a discourse tree from a subset of the text. The discourse treeincludes nodes, each nonterminal node representing a rhetoricalrelationship between two of the fragments and each terminal node of thenodes of the discourse tree is associated with one of the fragments. Themethod forms a communicative discourse tree that represents the subsetof text by matching each fragment that has a verb to a verb signature.The method identifies that the subset of the text includes a request foran explanation by applying a classification model trained to detect arequest for an explanation to the communicative discourse tree.

In another aspect, the matching includes accessing verb signatures. Eachverb signature includes the verb of the respective fragment and asequence of thematic roles. Thematic roles describe a relationshipbetween the verb and related words. The matching further includesdetermining, for each verb signature of the plurality of verbsignatures, thematic roles of the signature that matches a role of aword in a respective fragment. The matching further includes selecting aparticular verb signature from verb signatures based on the particularverb signature comprising a highest number of matches. The matchingfurther includes associating the particular verb signature with thefragment.

In another aspect, each verb signature of the verb signatures includesone of (i) an adverb, (ii) a noun phrase, or (iii) a noun. Associatingthe particular verb signature with the fragment further includesidentifying thematic role in the particular verb signature and matching,for each of thematic roles in the particular verb signature, acorresponding word in the fragment to the thematic role.

In another aspect, the classification model is a support vector machinewith tree kernel learning or uses nearest neighbor learning of maximalcommon sub-trees.

In another aspect, applying the classification model to the subset ofthe text further includes determining similarities between thecommunicative discourse tree and one or more communicative discoursetrees from a training set of communicative discourse trees. The applyingfurther includes selecting an additional communicative discourse treefrom the one or more communicative discourse trees based on theadditional communicative discourse tree having a highest number ofsimilarities with the communicative discourse tree. The applying furtherincludes identifying whether the communicative discourse tree is from apositive set or a negative set by applying a classification model to thecommunicative discourse tree. The positive set includes communicativediscourse trees representing text containing a request for anexplanation and the negative set includes communicative discourse treesrepresenting text without a request for an explanation. The applyingfurther includes determining, based on the identifying, whether the textcontains a request for an explanation.

In another aspect, accessing the text includes receiving text from auser device, the method further includes adjusting a response based onthe determined request for explanation and providing the adjustedresponse to a user device.

In another aspect, applying the classification model to the subset ofthe text further includes determining similarities between thecommunicative discourse tree and one or more communicative discoursetrees from a training set of communicative discourse trees. Applying theclassification model further includes selecting an additionalcommunicative discourse tree from the one or more communicativediscourse trees based on the additional communicative discourse treehaving a highest number of similarities with the communicative discoursetree. Applying the classification model further includes identifyingwhether the additional communicative discourse tree is from a positiveset or a negative set. The positive set is associated with textcontaining a request for explanation and the negative set is associatedwith text not containing a request for explanation. Applying theclassification model further includes determining, based on theidentifying, whether the text contains a request for explanation.

In another aspect, a method of building a training dataset includesaccessing text including fragments. The method includes creating adiscourse tree from the text. The discourse tree includes nodes, eachnonterminal node representing a rhetorical relationship between two ofthe fragments and each terminal node of the nodes of the discourse treeis associated with one of the fragments. The method further includesmatching each fragment that has a verb to a verb signature, therebycreating a communicative discourse tree. The method further includesaccessing a positive communicative discourse tree from a positive setand a negative communicative discourse tree from a negative set. Themethod further includes identifying whether the communicative discoursetree is from a positive set or a negative set by applying aclassification model to the communicative discourse tree. The positiveset includes communicative discourse trees representing text containinga request for an explanation and the negative set includes communicativediscourse trees representing text without a request for an explanation.The method further includes adding the communicative discourse tree toeither the positive training set or the negative training set based onthe identifying.

In another aspect, a method trains a classification model by iterativelyperforming a set of steps. The steps include providing one of a set oftraining pairs to the classification model. Each training pair includesa communicative discourse tree and an expected strength of a request foran explanation. The steps further include receiving, from theclassification model, a classification strength of a request forexplanation. The steps further include calculating a loss function bycalculating a difference between the expected strength and theclassification strength. The steps further include adjusting internalparameters of the classification model to minimize the loss function.

The above methods can be implemented as tangible computer-readable mediaand/or operating within a computer processor and attached memory.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an exemplary rhetoric classification environment inaccordance with an aspect.

FIG. 2 depicts an example of a discourse tree in accordance with anaspect.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect.

FIG. 4 depicts illustrative schemas in accordance with an aspect.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect.

FIG. 7 depicts an exemplary DT for an example request about property taxin accordance with an aspect.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7 .

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect.

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect.

FIG. 11 illustrates a communicative discourse tree for a claim of afirst agent in accordance with an aspect.

FIG. 12 illustrates a communicative discourse tree for a claim of asecond agent in accordance with an aspect.

FIG. 13 illustrates a communicative discourse tree for a claim of athird agent in accordance with an aspect.

FIG. 14 illustrates parse thickets in accordance with an aspect.

FIG. 15 illustrates an exemplary process for building a communicativediscourse tree in accordance with an aspect.

FIG. 16 illustrates a discourse tree and scenario graph in accordancewith an aspect.

FIG. 17 illustrates a maximal common sub-communicative discourse tree inaccordance with an aspect.

FIG. 18 illustrates a tree in a kernel learning format for acommunicative discourse tree in accordance with an aspect.

FIG. 19 illustrates an example of an electronic communication session inaccordance with an aspect.

FIG. 20 illustrates an example of an electronic translation of a phrasein accordance with an aspect.

FIG. 21 illustrates an example of a search result of a phrase inaccordance with an aspect.

FIG. 22 illustrates an exemplary process used to determine a presence ofa request for explanation in text in accordance with an aspect.

FIG. 23 illustrates an example of a linguistic representation of text inaccordance with an aspect.

FIG. 24 illustrates an exemplary process used to generate training datato train a classification model to determine a presence of a request forexplanation in text in accordance with an aspect.

FIG. 25 illustrates an exemplary process used to train a classificationmodel to determine a presence of a request for explanation in text inaccordance with an aspect.

FIG. 26 depicts a simplified diagram of a distributed system forimplementing one of the aspects.

FIG. 27 is a simplified block diagram of components of a systemenvironment by which services provided by the components of an aspectsystem may be offered as cloud services in accordance with an aspect.

FIG. 28 illustrates an exemplary computer system, in which variousaspects of the present invention may be implemented.

DETAILED DESCRIPTION

Aspects disclosed herein provide technical improvements to the area ofcomputer-implemented linguistics. More specifically, aspects of thepresent disclosure use communicative discourse trees in conjunction withmachine learning to determine a request for explanation in a particularargument detected in a body of text. Certain aspects can automaticallyclassify whether query, such as a complaint, contains a request forexplanation, whether implicit or explicit. An example of an implicitrequest for explanation is a set of negative comments or text about anexperience. An example of an explicit request explanation is a questionabout why a certain decision was made that prompted the user to initiatea complaint.

Typically, when a user device connects to a customer support system, auser either has a question, complaint, or both. Many times the complaintwill contain argumentation patterns because the user wants to bolsterhis or her claim when engaging in dialogue. The full text in thecomplaint may include a statement about a promise, a statement about howthe promise was not kept, and a statement about the result to thecustomer or user. In some examples, the customer who sent the query isconfused or in disagreement about a certain decision made and wouldeither like the decision reversed or have the decision explained.

Certain aspects represent text as communicative discourse trees.“Communicative discourse trees” or “CDTs” include discourse trees thatare supplemented with communicative actions. A communicative action is acooperative action undertaken by individuals based on mutualdeliberation and argumentation. Using communicative discourse trees,further aspects disclosed herein implement improved automated agents, orchatbots, that can answer questions and provide explanations. Inparticular, communicative discourse trees enable the identification ofelaboration rhetoric relations, which can in part indicate a request foran explanation.

In an example, a rhetoric classification application executing on acomputing device receives a question or text from a user. The rhetoricclassification application generates a communicative discourse tree forthe text. A communicative discourse tree is a discourse tree thatincludes communicative actions. Using a classification model, therhetoric agreement application determines whether the input textincludes a request for an explanation. The rhetoric agreement classifiercan provide this indication to a system that can obtain a suitableanswer for the user and provide the answer to the user, for example, viaa display device.

For example, some aspects use communicative discourse trees to helpisolate rhetorical features that indicate a request for explanation insequences like the following: “A BigWidget device has a laser lightfeature. Really? According to Sam, the BigWidget has the laser lightfeature. I don't believe that Sam said that BigWidget has the laserlight feature and Jane said that the BigWidget does not have the laserlight feature. I don't believe either. It does not work like this.(rhetorical relation of Contrast and Cause). Why would the BigWidgethave laser light? Is laser light possible? Please clarify. I don'tunderstand why BigWidget does not have the laser light feature.”

Continuing the example, a rhetoric classification application determinesthat the sentence “I don't believe that Sam said . . . ” has therhetorical relation of attribution and the sentences “I don't believeeither” and “It does not work like this” have the rhetorical relationsof contrast and cause. Further, rhetoric classification application 102can determine that the sentence “I don't understand why BigWidget . . .” has a particular mental state. All these features can be provided intoa classification model to determine whether text contains a request forexplanation (as in this case).

Certain Definitions

As used herein, “rhetorical structure theory” is an area of research andstudy that provided a theoretical basis upon which the coherence of adiscourse could be analyzed.

As used herein, “discourse tree” or “DT” refers to a structure thatrepresents the rhetorical relations for a sentence of part of asentence.

As used herein, a “rhetorical relation,” “rhetorical relationship,” or“coherence relation” or “discourse relation” refers to how two segmentsof discourse are logically connected to one another. Examples ofrhetorical relations include elaboration, contrast, and attribution.

As used herein, a “sentence fragment,” or “fragment” is a part of asentence that can be divided from the rest of the sentence. A fragmentis an elementary discourse unit. For example, for the sentence “Dutchaccident investigators say that evidence points to pro-Russian rebels asbeing responsible for shooting down the plane,” two fragments are “Dutchaccident investigators say that evidence points to pro-Russian rebels”and “as being responsible for shooting down the plane.” A fragment can,but need not, include a verb.

As used herein, “signature” or “frame” refers to a property of a verb ina fragment. Each signature can include one or more thematic roles. Forexample, for the fragment “Dutch accident investigators say thatevidence points to pro-Russian rebels,” the verb is “say” and thesignature of this particular use of the verb “say” could be “agent verbtopic” where “investigators” is the agent and “evidence” is the topic.

As used herein, “thematic role” refers to components of a signature usedto describe a role of one or more words. Continuing the previousexample, “agent” and “topic” are thematic roles.

As used herein, “nuclearity” refers to which text segment, fragment, orspan, is more central to a writer's purpose. The nucleus is the morecentral span, and the satellite is the less central one.

As used herein, “coherency” refers to the linking together of tworhetorical relations.

As used herein, “communicative verb” is a verb that indicatescommunication. For example, the verb “deny” is a communicative verb.

As used herein, “communicative action” describes an action performed byone or more agents and the subjects of the agents.

FIG. 1 shows an exemplary rhetoric classification environment inaccordance with an aspect. FIG. 1 depicts input text 130, rhetoricclassification computing device 101, and output response 170. Rhetoricclassification computing device 101 includes one or more of rhetoricclassification application 102, rhetoric agreement classifier 120, andtraining data 125. Rhetoric classification computing device 101 candetermine a presence of a request for explanation in input text 130 andcause one or more actions to occur, such as a search for additionalinformation, or the generation of an output response. An example of sucha process is discussed further with respect to FIG. 23 .

In an example, rhetoric classification application 102 analyzes aquestion received via chat. More specifically, rhetoric classificationapplication 102 receives input text 130, which can be a single questionor a stream of text. Input text 130 can be generated by any mobiledevice such as a mobile phone, smart phone, tablet, laptop, smart watch,and the like. A mobile device can communicate via a data network torhetoric classification computing device 101.

A request for explanation can be implicit, e.g., as illustrated by userfrustration as in the above case. For example, rhetoric classificationapplication 102 accesses input text 130, which reads: “Wow. Apparently,NewZeeBank just cancelled my credit cards despite an 18 yearrelationship. My account got flagged for money laundering. Good thing Iwas brought an extra credit card on my business trip and that thingcalled cash.”

In response, rhetoric classification application 102 creates acommunicative discourse tree 110 from input text 130. An example of aprocess that can be used is described in FIG. 15 . By representing inputtext 130 as communicative discourse tree 110, rhetoric classificationapplication 102 recognizes rhetorical relations between fragments ininput text 130 and associated communicative actions, as furtherdescribed herein.

Rhetoric classification application 102 provides communicative discoursetree 110 to a trained classifier such as rhetoric agreement classifier120. Rhetoric classification application 102 receives a prediction ofwhether a request for explanation is present from rhetoric agreementclassifier 120. In an example, rhetoric agreement classifier 120compares communicative discourse tree 110 with communicative discoursetrees provided by training data 125. Training data 125 includes atraining set designated as positive (including requests for anexplanation) or negative (without requests for an explanation). Anexemplary process for generating training data is discussed with respectto FIG. 24 and an exemplary process for training rhetoric agreementclassifier 120 is discussed with respect to FIG. 25 .

In turn, rhetoric classification application 102 provides the predictionas explanation request indicator 165. Based on explanation requestindicator 165, rhetoric classification application 102 can prepare anexplanation, cause an explanation to be prepared, or provide theexplanation as output response 170.

Rhetoric Structure Theory and Discourse Trees

Linguistics is the scientific study of language. For example,linguistics can include the structure of a sentence (syntax), e.g.,subject-verb-object, the meaning of a sentence (semantics), e.g. dogbites man vs. man bites dog, and what speakers do in conversation, i.e.,discourse analysis or the analysis of language beyond the sentence.

The theoretical underpinnings of discourse, Rhetoric Structure Theory(RST), can be attributed to Mann, William and Thompson, Sandra,“Rhetorical structure theory: A Theory of Text organization,”Text-Interdisciplinary Journal for the Study of Discourse, 8(3):243-281,1988. Similar to how the syntax and semantics of programming languagetheory helped enable modern software compilers, RST helped enabled theanalysis of discourse. More specifically RST posits structural blocks onat least two levels, a first level such as nuclearity and rhetoricalrelations, and a second level of structures or schemas. Discourseparsers or other computer software can parse text into a discourse tree.

Rhetoric Structure Theory models logical organization of text, astructure employed by a writer, relying on relations between parts oftext. RST simulates text coherence by forming a hierarchical, connectedstructure of texts via discourse trees. Rhetoric relations are splitinto the classes of coordinate and subordinate; these relations holdacross two or more text spans and therefore implement coherence. Thesetext spans are called elementary discourse units (EDUs). Clauses in asentence and sentences in a text are logically connected by the author.The meaning of a given sentence is related to that of the previous andthe following sentences. This logical relation between clauses is calledthe coherence structure of the text. RST is one of the most populartheories of discourse, being based on a tree-like discourse structure,discourse trees (DTs). The leaves of a DT correspond to EDUs, thecontiguous atomic text spans. Adjacent EDUs are connected by coherencerelations (e.g., Attribution, Sequence), forming higher-level discourseunits. These units are then also subject to this relation linking. EDUslinked by a relation are then differentiated based on their relativeimportance: nuclei are the core parts of the relation, while satellitesare peripheral ones. As discussed, in order to determine accuraterequest-response pairs, both topic and rhetorical agreement areanalyzed. When a speaker answers a question, such as a phrase or asentence, the speaker's answer should address the topic of thisquestion. In the case of an implicit formulation of a question, via aseed text of a message, an appropriate answer is expected not onlymaintain a topic, but also match the generalized epistemic state of thisseed.

Rhetoric Relations

As discussed, aspects described herein use communicative discoursetrees. Rhetorical relations can be described in different ways. Forexample, Mann and Thompson describe twenty-three possible relations. C.Mann, William & Thompson, Sandra. (1987) (“Mann and Thompson”).Rhetorical Structure Theory: A Theory of Text Organization. Othernumbers of relations are possible.

Relation Name Nucleus Satellite Antithesis ideas favored by the ideasdisfavored by the author author Background text whose under- text forfacilitating standing is being understanding facilitated Circumstancetext expressing the an interpretive context of events or ideas situationor time occurring in the interpretive context Concession situationaffirmed situation which is apparently by author inconsistent but alsoaffirmed by author Condition action or situation conditioning situationwhose occurrence results from the occurrence of the conditioningsituation Elaboration basic information additional informationEnablement an action information intended to aid the reader inperforming an action Evaluation a situation an evaluative comment aboutthe situation Evidence a claim information intended to increase thereader's belief in the claim Interpretation a situation aninterpretation of the situation Justify text information supporting thewriter's right to express the text Motivation an action informationintended to increase the reader's desire to perform the action Non- asituation another situation which causes volitional that one, but not byanyone's Cause deliberate action Non- a situation another situationwhich is caused volitional by that one, but not by anyone's Resultdeliberate action Otherwise action or situation conditioning situation(anti whose occurrence conditional) results from the lack of occurrenceof the conditioning situation Purpose an intended situation the intentbehind the situation Restatement a situation a reexpression of thesituation Solutionhood a situation or method a question, request,problem, or supporting full or other expressed need partial satisfactionof the need Summary text a short summary of that text Volitional asituation another situation which causes Cause that one, by someone'sdeliberate action Volitional a situation another situation which iscaused Result by that one, by someone's deliberate action

Some empirical studies postulate that the majority of text is structuredusing nucleus-satellite relations. See Mann and Thompson. But otherrelations do not carry a definite selection of a nucleus. Examples ofsuch relations are shown below.

Relation Name Span Other Span Contrast One alternate The other alternateJoint (unconstrained) (unconstrained) List An item A next item SequenceAn item A next item

FIG. 2 depicts an example of a discourse tree in accordance with anaspect. FIG. 2 includes discourse tree 200. Discourse tree includes textspan 201, text span 202, text span 203, relation 210 and relation 238.The numbers in FIG. 2 correspond to the three text spans. FIG. 3corresponds to the following example text with three text spans numbered1, 2, 3:

1. Honolulu, Hi. will be site of the 2017 Conference on Hawaiian History

2. It is expected that 200 historians from the U.S. and Asia will attend

3. The conference will be concerned with how the Polynesians sailed toHawaii

For example, relation 210, or elaboration, describes the relationshipbetween text span 201 and text span 202. Relation 238 depicts therelationship, elaboration, between text span 203 and 204. As depicted,text spans 202 and 203 elaborate further on text span 201. In the aboveexample, given a goal of notifying readers of a conference, text span 1is the nucleus. Text spans 2 and 3 provide more detail about theconference. In FIG. 2 , a horizontal number, e.g., 1-3, 1, 2, 3 covers aspan of text (possibly made up of further spans); a vertical linesignals the nucleus or nuclei; and a curve represents a rhetoricrelation (elaboration) and the direction of the arrow points from thesatellite to the nucleus. If the text span only functions as a satelliteand not as a nuclei, then deleting the satellite would still leave acoherent text. If from FIG. 2 one deletes the nucleus, then text spans 2and 3 are difficult to understand.

FIG. 3 depicts a further example of a discourse tree in accordance withan aspect. FIG. 3 includes components 301 and 302, text spans 305-307,relation 310 and relation 328. Relation 310 depicts the relationship,enablement, between components 306 and 305, and 307, and 305. FIG. 3refers to the following text spans:

1. The new Tech Report abstracts are now in the journal area of thelibrary near the abridged dictionary.

2. Please sign your name by any means that you would be interested inseeing.

3. Last day for sign-ups is 31 May.

As can be seen, relation 328 depicts the relationship between entity 307and 306, which is enablement. FIG. 3 illustrates that while nuclei canbe nested, there exists only one most nuclear text span.

Constructing a Discourse Tree

Discourse trees can be generated using different methods. A simpleexample of a method to construct a DT bottom up is:

(1) Divide the discourse text into units by:

-   -   (a) Unit size may vary, depending on the goals of the analysis    -   (b) Typically, units are clauses

(2) Examine each unit, and its neighbors. Is there a relation holdingbetween them?

(3) If yes, then mark that relation.

(4) If not, the unit might be at the boundary of a higher-levelrelation. Look at relations holding between larger units (spans).

(5) Continue until all the units in the text are accounted for.

Mann and Thompson also describe the second level of building blockstructures called schemas applications. In RST, rhetoric relations arenot mapped directly onto texts; they are fitted onto structures calledschema applications, and these in turn are fitted to text. Schemaapplications are derived from simpler structures called schemas (asshown by FIG. 4 ). Each schema indicates how a particular unit of textis decomposed into other smaller text units. A rhetorical structure treeor DT is a hierarchical system of schema applications. A schemaapplication links a number of consecutive text spans, and creates acomplex text span, which can in turn be linked by a higher-level schemaapplication. RST asserts that the structure of every coherent discoursecan be described by a single rhetorical structure tree, whose top schemacreates a span encompassing the whole discourse.

FIG. 4 depicts illustrative schemas in accordance with an aspect. FIG. 4shows a joint schema is a list of items consisting of nuclei with nosatellites. FIG. 4 depicts schemas 401-406. Schema 401 depicts acircumstance relation between text spans 410 and 428. Scheme 402 depictsa sequence relation between text spans 420 and 421 and a sequencerelation between text spans 421 and 423. Schema 403 depicts a contrastrelation between text spans 430 and 431. Schema 404 depicts a jointrelationship between text spans 440 and 441. Schema 405 depicts amotivation relationship between 450 and 451, and an enablementrelationship between 452 and 451. Schema 406 depicts joint relationshipbetween text spans 460 and 462. An example of a joint scheme is shown inFIG. 4 for the three text spans below:

1. Skies will be partly sunny in the New York metropolitan area today.

2. It will be more humid, with temperatures in the middle 80's.

3. Tonight will be mostly cloudy, with the low temperature between 65and 70.

While FIGS. 2-4 depict some graphical representations of a discoursetree, other representations are possible.

FIG. 5 depicts a node-link representation of the hierarchical binarytree in accordance with an aspect. As can be seen from FIG. 5 , theleaves of a DT correspond to contiguous non-overlapping text spanscalled Elementary Discourse Units (EDUs). Adjacent EDUs are connected byrelations (e.g., elaboration, attribution . . . ) and form largerdiscourse units, which are also connected by relations. “Discourseanalysis in RST involves two sub-tasks: discourse segmentation is thetask of identifying the EDUs, and discourse parsing is the task oflinking the discourse units into a labeled tree.” See Joty, Shafiq R andGiuseppe Carenini, Raymond T Ng, and Yashar Mehdad. 2013. Combiningintra- and multisentential rhetorical parsing for document-leveldiscourse analysis. In ACL (1), pages 486-496.

FIG. 5 depicts text spans that are leaves, or terminal nodes, on thetree, each numbered in the order they appear in the full text, shown inFIG. 6 . FIG. 5 includes tree 500. Tree 500 includes, for example, nodes501-507. The nodes indicate relationships. Nodes are non-terminal, suchas node 501, or terminal, such as nodes 502-507. As can be seen, nodes503 and 504 are related by a joint relationship. Nodes 502, 505, 506,and 508 are nuclei. The dotted lines indicate that the branch or textspan is a satellite. The relations are nodes in gray boxes.

FIG. 6 depicts an exemplary indented text encoding of the representationin FIG. 5 in accordance with an aspect. FIG. 6 includes text 600 andtext sequences 602-604. Text 600 is presented in a manner more amenableto computer programming. Text sequence 602 corresponds to node 502,sequence 603 corresponds to node 503, and sequence 604 corresponds tonode 504. In FIG. 6 , “N” indicates a nucleus and “S” indicates asatellite.

Examples of Discourse Parsers

Automatic discourse segmentation can be performed with differentmethods. For example, given a sentence, a segmentation model identifiesthe boundaries of the composite elementary discourse units by predictingwhether a boundary should be inserted before each particular token inthe sentence. For example, one framework considers each token in thesentence sequentially and independently. In this framework, thesegmentation model scans the sentence token by token, and uses a binaryclassifier, such as a support vector machine or logistic regression, topredict whether it is appropriate to insert a boundary before the tokenbeing examined. In another example, the task is a sequential labelingproblem. Once text is segmented into elementary discourse units,sentence-level discourse parsing can be performed to construct thediscourse tree. Machine learning techniques can be used.

In one aspect of the present invention, two Rhetorical Structure Theory(RST) discourse parsers are used: CoreNLPProcessor which relies onconstituent syntax, and FastNLPProcessor which uses dependency syntax.See Surdeanu, Mihai & Hicks, Thomas & Antonio Valenzuela-Escarcega,Marco. Two Practical Rhetorical Structure Theory Parsers. (2015).

In addition, the above two discourse parsers, i.e., CoreNLPProcessor andFastNLPProcessor use Natural Language Processing (NLP) for syntacticparsing. For example, the Stanford CoreNLP gives the base forms ofwords, their parts of speech, whether they are names of companies,people, etc., normalize dates, times, and numeric quantities, mark upthe structure of sentences in terms of phrases and syntacticdependencies, indicate which noun phrases refer to the same entities.Practically, RST is a still theory that may work in many cases ofdiscourse, but in some cases, it may not work. There are many variablesincluding, but not limited to, what EDU's are in a coherent text, i.e.,what discourse segmenters are used, what relations inventory is used andwhat relations are selected for the EDUs, the corpus of documents usedfor training and testing, and even what parsers are used. So forexample, in Surdeanu, et al., “Two Practical Rhetorical Structure TheoryParsers,” paper cited above, tests must be run on a particular corpususing specialized metrics to determine which parser gives betterperformance. Thus unlike computer language parsers which givepredictable results, discourse parsers (and segmenters) can giveunpredictable results depending on the training and/or test text corpus.Thus, discourse trees are a mixture of the predicable arts (e.g.,compilers) and the unpredictable arts (e.g., like chemistry wereexperimentation is needed to determine what combinations will give youthe desired results).

In order to objectively determine how good a Discourse analysis is, aseries of metrics are being used, e.g., Precision/Recall/F1 metrics fromDaniel Marcu, “The Theory and Practice of Discourse Parsing andSummarization,” MIT Press, (2000). Precision, or positive predictivevalue is the fraction of relevant instances among the retrievedinstances, while recall (also known as sensitivity) is the fraction ofrelevant instances that have been retrieved over the total amount ofrelevant instances. Both precision and recall are therefore based on anunderstanding and measure of relevance. Suppose a computer program forrecognizing dogs in photographs identifies eight dogs in a picturecontaining 12 dogs and some cats. Of the eight dogs identified, fiveactually are dogs (true positives), while the rest are cats (falsepositives). The program's precision is ⅝ while its recall is 5/12. Whena search engine returns 30 pages only 20 of which were relevant whilefailing to return 40 additional relevant pages, its precision is 20/30=⅔while its recall is 20/60=⅓. Therefore, in this case, precision is ‘howuseful the search results are’, and recall is ‘how complete the resultsare.’” The F1 score (also F-score or F-measure) is a measure of a test'saccuracy. It considers both the precision and the recall of the test tocompute the score: F1=2×((precision×recall)/(precision+recall)) and isthe harmonic mean of precision and recall. The F1 score reaches its bestvalue at 1 (perfect precision and recall) and worst at 0.

Autonomous Agents or Chatbots

A conversation between Human A and Human B is a form of discourse. Forexample, applications exist such as FaceBook® Messenger, WhatsApp®,Slack,® SMS, etc., a conversation between A and B may typically be viamessages in addition to more traditional email and voice conversations.A chatbot (which may also be called intelligent bots or virtualassistant, etc.) is an “intelligent” machine that, for example, replaceshuman B and to various degrees mimics the conversation between twohumans. An example ultimate goal is that human A cannot tell whether Bis a human or a machine (the Turning test, developed by Alan Turing in1950). Discourse analysis, artificial intelligence, including machinelearning, and natural language processing, have made great stridestoward the long-term goal of passing the Turing test. Of course, withcomputers being more and more capable of searching and processing vastrepositories of data and performing complex analysis on the data toinclude predictive analysis, the long-term goal is the chatbot beinghuman-like and a computer combined.

For example, users can interact with the Intelligent Bots Platformthrough a conversational interaction. This interaction, also called theconversational user interface (UI), is a dialog between the end user andthe chatbot, just as between two human beings. It could be as simple asthe end user saying “Hello” to the chatbot and the chatbot respondingwith a “Hi” and asking the user how it can help, or it could be atransactional interaction in a banking chatbot, such as transferringmoney from one account to the other, or an informational interaction ina HR chatbot, such as checking for vacation balance, or asking an FAQ ina retail chatbot, such as how to handle returns. Natural languageprocessing (NLP) and machine learning (ML) algorithms combined withother approaches can be used to classify end user intent. An intent at ahigh level is what the end user would like to accomplish (e.g., getaccount balance, make a purchase). An intent is essentially, a mappingof customer input to a unit of work that the backend should perform.Therefore, based on the phrases uttered by the user in the chatbot,these are mapped that to a specific and discrete use case or unit ofwork, for e.g. check balance, transfer money and track spending are all“use cases” that the chatbot should support and be able to work outwhich unit of work should be triggered from the free text entry that theend user types in a natural language.

The underlying rational for having an AI chatbot respond like a human isthat the human brain can formulate and understand the request and thengive a good response to the human request much better than a machine.Thus, there should be significant improvement in the request/response ofa chatbot, if human B is mimicked. So an initial part of the problem ishow does the human brain formulate and understand the request? To mimic,a model is used. RST and DT allow a formal and repeatable way of doingthis.

At a high level, there are typically two types of requests: (1) Arequest to perform some action; and (2) a request for information, e.g.,a question. The first type has a response in which a unit of work iscreated. The second type has a response that is, e.g., a good answer, tothe question. The answer could take the form of, for example, in someaspects, the AI constructing an answer from its extensive knowledgebase(s) or from matching the best existing answer from searching theinternet or intranet or other publically/privately available datasources.

Communicative Discourse Trees and the Rhetoric Classifier

Aspects of the present disclosure build communicative discourse treesand use communicative discourse trees to analyze whether the rhetoricalstructure of a request or question agrees with an answer. Morespecifically, aspects described herein create representations of arequest-response pair, learns the representations, and relates the pairsinto classes of valid or invalid pairs. In this manner, an autonomousagent can receive a question from a user, process the question, forexample, by searching for multiple answers, determine the best answerfrom the answers, and provide the answer to the user.

More specifically, to represent linguistic features of text, aspectsdescribed herein use rhetoric relations and speech acts (orcommunicative actions). Rhetoric relations are relationships between theparts of the sentences, typically obtained from a discourse tree. Speechacts are obtained as verbs from a verb resource such as VerbNet. Byusing both rhetoric relations and communicative actions, aspectsdescribed herein can correctly recognize valid request-response pairs.To do so, aspects correlate the syntactic structure of a question withthat of an answer. By using the structure, a better answer can bedetermined.

For example, when an autonomous agent receives an indication from aperson that the person desires to sell an item with certain features,the autonomous agent should provide a search result that not onlycontains the features but also indicates an intent to buy. In thismanner, the autonomous agent has determined the user's intent.Similarly, when an autonomous agent receives a request from a person toshare knowledge about a particular item, the search result shouldcontain an intent to receive a recommendation. When a person asks anautonomous agent for an opinion about a subject, the autonomous agentshares an opinion about the subject, rather than soliciting anotheropinion.

Analyzing Request and Response Pairs

FIG. 7 depicts an exemplary DT for an example request about property taxin accordance with an aspect. The node labels are the relations and thearrowed line points to the satellite. The nucleus is a solid line. FIG.7 depicts the following text.

Request: “My husbands' grandmother gave him his grandfather's truck. Shesigned the title over but due to my husband having unpaid fines on hislicense, he was not able to get the truck put in his name. I wanted toput in my name and paid the property tax and got insurance for thetruck. By the time it came to sending off the title and getting the tag,I didn't have the money to do so. Now, due to circumstances, I am notgoing to be able to afford the truck. I went to the insurance place andwas refused a refund. I am just wondering that since I am not going tohave a tag on this truck, is it possible to get the property taxrefunded?”

Response: “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax. If you apply late, therewill be penalties on top of the normal taxes and fees. You don't need toregister it at the same time, but you absolutely need to title it withinthe period of time stipulated in state law.”

As can be seen in FIG. 7 , analyzing the above text results in thefollowing. “My husbands' grandmother gave him his grandfather's truck”is elaborated by “She signed the title over but due to my husband”elaborated by “having unpaid fines on his license, he was not able toget the truck put in his name.” which is elaborated by “I wanted to putin my name,” “and paid the property tax”, and “and got insurance for thetruck.”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck.” iselaborated by;

“I didn't have the money” elaborated by “to do so” contrasted with

“By the time” elaborated by “it came to sending off the title”

“and getting the tag”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so” is contrasted with

“Now, due to circumstances,” elaborated with “I am not going to be ableto afford the truck.” which is elaborated with

“I went to the insurance place”

“and was refused a refund”

“My husbands' grandmother gave him his grandfather's truck. She signedthe title over but due to my husband having unpaid fines on his license,he was not able to get the truck put in his name. I wanted to put in myname and paid the property tax and got insurance for the truck. By thetime it came to sending off the title and getting the tag, I didn't havethe money to do so. Now, due to circumstances, I am not going to be ableto afford the truck. I went to the insurance place and was refused arefund.” is elaborated with

“I am just wondering that since I am not going to have a tag on thistruck, is it possible to get the property tax refunded?”

“I am just wondering” has attribution to

“that” is the same unit as “is it possible to get the property taxrefunded?” which has condition “since I am not going to have a tag onthis truck”

As can be seen, the main subject of the topic is “Property tax on acar”. The question includes the contradiction: on one hand, allproperties are taxable, and on the other hand, the ownership is somewhatincomplete. A good response has to address both topic of the questionand clarify the inconsistency. To do that, the responder is making evenstronger claim concerning the necessity to pay tax on whatever is ownedirrespectively of the registration status. This example is a member ofpositive training set from our Yahoo! Answers evaluation domain. Themain subject of the topic is “Property tax on a car”. The questionincludes the contradiction: on one hand, all properties are taxable, andon the other hand, the ownership is somewhat incomplete. A goodanswer/response has to address both topic of the question and clarifythe inconsistency. The reader can observe that since the questionincludes rhetoric relation of contrast, the answer has to match it witha similar relation to be convincing. Otherwise, this answer would lookincomplete even to those who are not domain experts.

FIG. 8 depicts an exemplary response for the question represented inFIG. 7 , according to certain aspects of the present invention. Thecentral nucleus is “the property tax is assessed on property” elaboratedby “that you own”. “The property tax is assessed on property that youown” is also a nucleus elaborated by “Just because you chose to notregister it does not mean that you don't own it, so the tax is notrefundable. Even if you have not titled the vehicle yet, you still ownit within the boundaries of the tax district, so the tax is payable.Note that all states give you a limited amount of time to transfer titleand pay the use tax.”

The nucleus “The property tax is assessed on property that you own. Justbecause you chose to not register it does not mean that you don't ownit, so the tax is not refundable. Even if you have not titled thevehicle yet, you still own it within the boundaries of the tax district,so the tax is payable. Note that all states give you a limited amount oftime to transfer title and pay the use tax.” is elaborated by “therewill be penalties on top of the normal taxes and fees” with condition“If you apply late,” which in turn is elaborated by the contrast of “butyou absolutely need to title it within the period of time stipulated instate law.” and “You don't need to register it at the same time.”

Comparing the DT of FIG. 7 and DT of FIG. 8 , enables a determination ofhow well matched the response (FIG. 8 ) is to the request (FIG. 7 ). Insome aspects of the present invention, the above framework is used, atleast in part, to determine the DTs for the request/response and therhetoric agreement between the DTs.

In another example, the question “What does The Investigative Committeeof the Russian Federation do” has at least two answers, for example, anofficial answer or an actual answer.

FIG. 9 illustrates a discourse tree for an official answer in accordancewith an aspect. As depicted in FIG. 9 , an official answer, or missionstatement states that “The Investigative Committee of the RussianFederation is the main federal investigating authority which operates asRussia's Anti-corruption agency and has statutory responsibility forinspecting the police forces, combating police corruption and policemisconduct, is responsible for conducting investigations into localauthorities and federal governmental bodies.”

FIG. 10 illustrates a discourse tree for a raw answer in accordance withan aspect. As depicted in FIG. 10 , another, perhaps more honest, answerstates that “Investigative Committee of the Russian Federation issupposed to fight corruption. However, top-rank officers of theInvestigative Committee of the Russian Federation are charged withcreation of a criminal community. Not only that, but their involvementin large bribes, money laundering, obstruction of justice, abuse ofpower, extortion, and racketeering has been reported. Due to theactivities of these officers, dozens of high-profile cases including theones against criminal lords had been ultimately ruined.”

The choice of answers depends on context. Rhetoric structure allowsdifferentiating between “official”, “politically correct”,template-based answers and “actual”, “raw”, “reports from the field”, or“controversial” answers, see FIGS. 9 and 10 ). Sometimes, the questionitself can give a hint about which category of answers is expected. If aquestion is formulated as a factoid or definitional one, without asecond meaning, then the first category of answers is suitable.Otherwise, if a question has the meaning “tell me what it really is”,then the second category is appropriate. In general, after extracting arhetoric structure from a question, selecting a suitable answer thatwould have a similar, matching, or complementary rhetoric structure iseasier.

The official answer is based on elaboration and joints, which areneutral in terms of controversy a text might contain (See FIG. 9 ). Atthe same time, the row answer includes the contrast relation. Thisrelation is extracted between the phrase for what an agent is expectedto do and what this agent was discovered to have done.

Communicative Discourse Trees (CDTs)

Rhetoric classification application 102 can create, analyze, and comparecommunicative discourse trees. Communicative discourse trees aredesigned to combine rhetoric information with speech act structures.CDTs include with arcs labeled with expressions for communicativeactions. By combining communicative actions, CDTs enable the modeling ofRST relations and communicative actions. A CDT is a reduction of a parsethicket. See Galitsky, B, Ilvovsky, D. and Kuznetsov S. O. Rhetoric Mapof an Answer to Compound Queries Knowledge Trail Inc. ACL 2015, 681-686.(“Galitsky 2015”). A parse thicket is a combination of parse trees forsentences with discourse-level relationships between words and parts ofthe sentence in one graph. By incorporating labels that identify speechactions, learning of communicative discourse trees can occur over aricher features set than just rhetoric relations and syntax ofelementary discourse units (EDUs).

In an example, a dispute between three parties concerning the causes ofa downing of a commercial airliner, Malaysia Airlines Flight 17 isanalyzed. An RST representation of the arguments being communicated isbuilt. In the example, three conflicting agents, Dutch investigators,The Investigative Committee of the Russian Federation, and theself-proclaimed Donetsk People's Republic exchange their opinions on thematter. The example illustrates a controversial conflict where eachparty does all it can to blame its opponent. To sound more convincing,each party does not just produce its claim but formulates a response ina way to rebuff the claims of an opponent. To achieve this goal, eachparty attempts to match the style and discourse of the opponents'claims.

FIG. 11 illustrates a communicative discourse tree for a claim of afirst agent in accordance with an aspect. FIG. 11 depicts communicativediscourse tree 100, which represents the following text: “Dutch accidentinvestigators say that evidence points to pro-Russian rebels as beingresponsible for shooting down plane. The report indicates where themissile was fired from and identifies who was in control of theterritory and pins the downing of MH17 on the pro-Russian rebels.”

As can be seen from FIG. 11 , non-terminal nodes of CDTs are rhetoricrelations, and terminal nodes are elementary discourse units (phrases,sentence fragments) which are the subjects of these relations. Certainarcs of CDTs are labeled with the expressions for communicative actions,including the actor agent and the subject of these actions (what isbeing communicated). For example, the nucleus node for elaborationrelation (on the left) are labeled with say (Dutch, evidence), and thesatellite with responsible (rebels, shooting down). These labels are notintended to express that the subjects of EDUs are evidence and shootingdown but instead for matching this CDT with others for the purpose offinding similarity between them. In this case just linking thesecommunicative actions by a rhetoric relation and not providinginformation of communicative discourse would be too limited way torepresent a structure of what and how is being communicated. Arequirement for an RR pair to have the same or coordinated rhetoricrelation is too weak, so an agreement of CDT labels for arcs on top ofmatching nodes is required.

The straight edges of this graph are syntactic relations, and curvy arcsare discourse relations, such as anaphora, same entity, sub-entity,rhetoric relation and communicative actions. This graph includes muchricher information than just a combination of parse trees for individualsentences. In addition to CDTs, parse thickets can be generalized at thelevel of words, relations, phrases and sentences. The speech actions arelogic predicates expressing the agents involved in the respective speechacts and their subjects. The arguments of logical predicates are formedin accordance to respective semantic roles, as proposed by a frameworksuch as VerbNet. See Karin Kipper, Anna Korhonen, Neville Ryant, MarthaPalmer, A Large-scale Classification of English Verbs, LanguageResources and Evaluation Journal, 42(1), pp. 21-40, Springer Netherland,2008, and/or Karin Kipper Schuler, Anna Korhonen, Susan W. Brown,VerbNet overview, extensions, mappings and apps, Tutorial, NAACL-HLT2009, Boulder, Colo.

FIG. 12 illustrates a communicative discourse tree for a claim of asecond agent in accordance with an aspect. FIG. 12 depicts communicativediscourse tree 1200, which represents the following text: “TheInvestigative Committee of the Russian Federation believes that theplane was hit by a missile, which was not produced in Russia. Thecommittee cites an investigation that established the type of themissile.”

FIG. 13 illustrates a communicative discourse tree for a claim of athird agent in accordance with an aspect. FIG. 13 depicts communicativediscourse tree 1300, which represents the following text: “Rebels, theself-proclaimed Donetsk People's Republic, deny that they controlled theterritory from which the missile was allegedly fired. It became possibleonly after three months after the tragedy to say if rebels controlledone or another town.”

As can be seen from communicative discourse trees 1100-1300, a responseis not arbitrary. A response talks about the same entities as theoriginal text. For example, communicative discourse trees 1200 and 1300are related to communicative discourse tree 1100. A response backs up adisagreement with estimates and sentiments about these entities, andabout actions of these entities.

More specifically, replies of involved agent need to reflect thecommunicative discourse of the first, seed message. As a simpleobservation, because the first agent uses Attribution to communicate hisclaims, the other agents have to follow the suite and either providetheir own attributions or attack the validity of attribution of theproponent, or both. To capture a broad variety of features for howcommunicative structure of the seed message needs to be retained inconsecutive messages, pairs of respective CDTs can be learned.

To verify the agreement of a request-response, discourse relations orspeech acts (communicative actions) alone are often insufficient. As canbe seen from the example depicted in FIGS. 11-13 , the discoursestructure of interactions between agents and the kind of interactionsare useful. However, the domain of interaction (e.g., military conflictsor politics) or the subjects of these interactions, i.e., the entities,do not need to be analyzed.

Representing Rhetoric Relations and Communicative Actions

In order to compute similarity between abstract structures, twoapproaches are frequently used: (1) representing these structures in anumerical space, and express similarity as a number, which is astatistical learning approach, or (2) using a structural representation,without numerical space, such as trees and graphs, and expressingsimilarity as a maximal common sub-structure. Expressing similarity as amaximal common sub-structure is referred to as generalization.

Learning communicative actions helps express and understand arguments.Computational verb lexicons help support acquisition of entities foractions and provide a rule-based form to express their meanings. Verbsexpress the semantics of an event being described as well as therelational information among participants in that event, and project thesyntactic structures that encode that information. Verbs, and inparticular communicative action verbs, can be highly variable and candisplay a rich range of semantic behaviors. In response, verbclassification helps a learning systems to deal with this complexity byorganizing verbs into groups that share core semantic properties.

VerbNet is one such lexicon, which identifies semantic roles andsyntactic patterns characteristic of the verbs in each class and makesexplicit the connections between the syntactic patterns and theunderlying semantic relations that can be inferred for all members ofthe class. See Karin Kipper, Anna Korhonen, Neville Ryant and MarthaPalmer, Language Resources and Evaluation, Vol. 42, No. 1 (March 2008),at 21. Each syntactic frame, or verb signature, for a class has acorresponding semantic representation that details the semanticrelations between event participants across the course of the event.

For example, the verb amuse is part of a cluster of similar verbs thathave a similar structure of arguments (semantic roles) such as amaze,anger, arouse, disturb, and irritate. The roles of the arguments ofthese communicative actions are as follows: Experiencer (usually, ananimate entity), Stimulus, and Result. Each verb can have classes ofmeanings differentiated by syntactic features for how this verb occursin a sentence, or frames. For example, the frames for amuse are asfollows, using the following key noun phrase (NP), noun (N),communicative action (V), verb phrase (VP), adverb (ADV):

NP V NP. Example: “The teacher amused the children.” Syntax: Stimulus VExperiencer. Clause: amuse(Stimulus, E, Emotion, Experiencer),cause(Stimulus, E), emotional_state(result(E), Emotion, Experiencer).

NP V ADV-Middle. Example: “Small children amuse quickly.” Syntax:Experiencer V ADV. Clause: amuse(Experiencer, Prop):-,property(Experiencer, Prop), adv(Prop).

NP V NP-PRO-ARB. Example “The teacher amused.” Syntax Stimulus V.amuse(Stimulus, E, Emotion, Experiencer): cause(Stimulus, E),emotional_state(result(E), Emotion, Experiencer).

NP.cause V NP. Example “The teacher's dolls amused the children.” syntaxStimulus <+genitive> ('s) V Experiencer. amuse(Stimulus, E, Emotion,Experiencer): cause(Stimulus, E), emotional_state(during(E), Emotion,Experiencer).

NP V NP ADJ. Example “This performance bored me totally.” syntaxStimulus V Experiencer Result. amuse(Stimulus, E, Emotion, Experiencer).cause(Stimulus, E), emotional_state(result(E), Emotion, Experiencer),Pred(result(E), Experiencer).

Communicative actions can be characterized into clusters, for example:

Verbs with Predicative Complements (Appoint, characterize, dub, declare,conjecture, masquerade, orphan, captain, consider, classify), Verbs ofPerception (See, sight, peer).

Verbs of Psychological State (Amuse, admire, marvel, appeal), Verbs ofDesire (Want, long).

Judgment Verbs (Judgment), Verbs of Assessment (Assess, estimate), Verbsof Searching (Hunt, search, stalk, investigate, rummage, ferret), Verbsof Social Interaction (Correspond, marry, meet, battle), Verbs ofCommunication (Transfer(message), inquire, interrogate, tell, manner(speaking), talk, chat, say, complain, advise, confess, lecture,overstate, promise). Avoid Verbs (Avoid), Measure Verbs, (Register,cost, fit, price, bill), Aspectual Verbs (Begin, complete, continue,stop, establish, sustain.

Aspects described herein provide advantages over statistical learningmodels. In contrast to statistical solutions, aspects use aclassification system can provide a verb or a verb-like structure whichis determined to cause the target feature (such as rhetoric agreement).For example, statistical machine learning models express similarity as anumber, which can make interpretation difficult.

Representing Request-Response Pairs

Representing request-response pairs facilitates classification basedoperations based on a pair. In an example, request-response pairs can berepresented as parse thickets. A parse thicket is a representation ofparse trees for two or more sentences with discourse-level relationshipsbetween words and parts of the sentence in one graph. See Galitsky 2015.Topical similarity between question and answer can expressed as commonsub-graphs of parse thickets. The higher the number of common graphnodes, the higher the similarity.

FIG. 14 illustrates parse thickets in accordance with an aspect. FIG. 14depicts parse thicket 1400 including a parse tree 1401 for a request,and a parse tree for a corresponding response 1402.

Parse tree 1401 represents the question “I just had a baby and it looksmore like the husband I had my baby with. However it does not look likeme at all and I am scared that he was cheating on me with another ladyand I had her kid. This child is the best thing that has ever happenedto me and I cannot imagine giving my baby to the real mom.”

Response 1402 represents the response “Marital therapists advise ondealing with a child being born from an affair as follows. One option isfor the husband to avoid contact but just have the basic legal andfinancial commitments. Another option is to have the wife fully involvedand have the baby fully integrated into the family just like a childfrom a previous marriage.”

FIG. 14 represents a greedy approach to representing linguisticinformation about a paragraph of text. The straight edges of this graphare syntactic relations, and curvy arcs are discourse relations, such asanaphora, same entity, sub-entity, rhetoric relation and communicativeactions. The solid arcs are for same entity/sub-entity/anaphorarelations, and the dotted arcs are for rhetoric relations andcommunicative actions. Oval labels in straight edges denote thesyntactic relations. Lemmas are written in the boxes for the nodes, andlemma forms are written on the right side of the nodes.

Parse thicket 1400 includes much richer information than just acombination of parse trees for individual sentences. Navigation throughthis graph along the edges for syntactic relations as well as arcs fordiscourse relations allows to transform a given parse thicket intosemantically equivalent forms for matching with other parse thickets,performing a text similarity assessment task. To form a complete formalrepresentation of a paragraph, as many links as possible are expressed.Each of the discourse arcs produces a pair of thicket phrases that canbe a potential match.

Topical similarity between the seed (request) and response is expressedas common sub-graphs of parse thickets. They are visualized as connectedclouds. The higher the number of common graph nodes, the higher thesimilarity. For rhetoric agreement, common sub-graph does not have to belarge as it is in the given text. However, rhetoric relations andcommunicative actions of the seed and response are correlated and acorrespondence is required.

Generalization for Communicative Actions

A similarity between two communicative actions A₁ and A₂ is defined as aan abstract verb which possesses the features which are common betweenA₁ and A₂. Defining a similarity of two verbs as an abstract verb-likestructure supports inductive learning tasks, such as a rhetoricagreement assessment. In an example, a similarity between the followingtwo common verbs, agree and disagree, can be generalized as follows:agree{circumflex over ( )}disagree=verb(Interlocutor, Proposed_action,Speaker), where Interlocution is the person who proposed theProposed_action to the Speaker and to whom the Speaker communicatestheir response. Proposed_action is an action that the Speaker wouldperform if they were to accept or refuse the request or offer, and TheSpeaker is the person to whom a particular action has been proposed andwho responds to the request or offer made.

In a further example, a similarity between verbs agree and explain isrepresented as follows: agree{circumflex over( )}explain=verb(Interlocutor, *, Speaker). The subjects ofcommunicative actions are generalized in the context of communicativeactions and are not be generalized with other “physical” actions. Hence,aspects generalize individual occurrences of communicative actionstogether with corresponding subjects.

Additionally, sequences of communicative actions representing dialogscan be compared against other such sequences of similar dialogs. In thismanner, the meaning of an individual communicative action as well as thedynamic discourse structure of a dialogue is (in contrast to its staticstructure reflected via rhetoric relations) is represented. Ageneralization is a compound structural representation that happens ateach level. Lemma of a communicative action is generalized with lemma,and its semantic role are generalized with respective semantic role.

Communicative actions are used by text authors to indicate a structureof a dialogue or a conflict. See Searle, J. R. 1969, Speech acts: anessay in the philosophy of language. London: Cambridge University Press.Subjects are generalized in the context of these actions and are notgeneralized with other “physical” actions. Hence, the individualoccurrences of communicative actions together are generalized with theirsubjects, as well as their pairs, as discourse “steps.”

Generalization of communicative actions can also be thought of from thestandpoint of matching the verb frames, such as VerbNet. Thecommunicative links reflect the discourse structure associated withparticipation (or mentioning) of more than a single agent in the text.The links form a sequence connecting the words for communicative actions(either verbs or multi-words implicitly indicating a communicativeintent of a person).

Communicative actions include an actor, one or more agents being actedupon, and the phrase describing the features of this action. Acommunicative action can be described as a function of the form: verb(agent, subject, cause), where verb characterizes some type ofinteraction between involved agents (e.g., explain, confirm, remind,disagree, deny, etc.), subject refers to the information transmitted orobject described, and cause refers to the motivation or explanation forthe subject.

A scenario (labeled directed graph) is a sub-graph of a parse thicketG=(V,A), where V={action₁, action₂ . . . action_(n)} is a finite set ofvertices corresponding to communicative actions, and A is a finite setof labeled arcs (ordered pairs of vertices), classified as follows:

Each arc action_(i), action_(j)∈A_(sequence) corresponds to a temporalprecedence of two actions v_(i), ag_(i), s_(i), c_(i) and v_(j), ag_(j),s_(j), c_(j) that refer to the same subject, e.g., s_(j)=s_(i) ordifferent subjects. Each arc action_(i), action_(j)∈A_(cause)corresponds to an attack relationship between action_(i) and action_(j)indicating that the cause of action_(i) in conflict with the subject orcause of action_(j).

Subgraphs of parse thickets associated with scenarios of interactionbetween agents have some distinguishing features. For example, (1) allvertices are ordered in time, so that there is one incoming arc and oneoutgoing arc for all vertices (except the initial and terminalvertices), (2) for A_(sequence) arcs, at most one incoming and only oneoutgoing arc are admissible, and (3) for A_(cause) arcs, there can bemany outgoing arcs from a given vertex, as well as many incoming arcs.The vertices involved may be associated with different agents or withthe same agent (i.e., when he contradicts himself). To computesimilarities between parse thickets and their communicative action,induced subgraphs, the sub-graphs of the same configuration with similarlabels of arcs and strict correspondence of vertices are analyzed.

The following similarities exist by analyzing the arcs of thecommunicative actions of a parse thicket: (1) one communicative actionfrom with its subject from T1 against another communicative action withits subject from T2 (communicative action arc is not used), and (2) apair of communicative actions with their subjects from T1 compared toanother pair of communicative actions from T2 (communicative action arcsare used).

Generalizing two different communicative actions is based on theirattributes. See (Galitsky et al 2013). As can be seen in the examplediscussed with respect to FIG. 14 , one communicative action from T1,cheating(husband, wife, another lady) can be compared with a second fromT2, avoid(husband, contact(husband, another lady)). A generalizationresults in communicative_action(husband, *) which introduces aconstraint on A in the form that if a given agent (=husband) ismentioned as a subject of CA in Q, he(she) should also be a subject of(possibly, another) CA in A. Two communicative actions can always begeneralized, which is not the case for their subjects: if theirgeneralization result is empty, the generalization result ofcommunicative actions with these subjects is also empty.

Generalization of RST Relations

Some relations between discourse trees can be generalized, such as arcsthat represent the same type of relation (presentation relation, such asantithesis, subject matter relation, such as condition, and multinuclearrelation, such as list) can be generalized. A nucleus or a situationpresented by a nucleus is indicated by “N.” Satellite or situationspresented by a satellite, are indicated by “S.” “W” indicates a writer.“R” indicates a reader (hearer). Situations are propositions, completedactions or actions in progress, and communicative actions and states(including beliefs, desires, approve, explain, reconcile and others).Generalization of two RST relations with the above parameters isexpressed as:rst1(N1,S1,W1,R1){circumflex over ( )}rst2(N2,S2,W2,R2)=(rst1{circumflexover ( )}rst2)(N1{circumflex over ( )}N2,S1{circumflex over( )}S2,W1{circumflex over ( )}W2,R1{circumflex over ( )}R2).

The texts in N1, S1, W1, R1 are subject to generalization as phrases.For example, rst1{circumflex over ( )}rst2 can be generalized asfollows: (1) if relation_type(rst1)!=relation_type(rst2) then ageneralization is empty. (2) Otherwise, the signatures of rhetoricrelations are generalized as sentences: sentence(N1, S1, W1,R1){circumflex over ( )}sentence(N2, S2, W2, R2). See Iruskieta, Mikel,Iria da Cunha and Maite Taboada. A qualitative comparison method forrhetorical structures: identifying different discourse structures inmultilingual corpora. Lang Resources & Evaluation. June 2015, Volume 49,Issue 2.

For example, the meaning of rst-background{circumflex over( )}rst-enablement=(S increases the ability of R to comprehend anelement in N){circumflex over ( )}(R comprehending S increases theability of R to perform the action in N)=increase-VB the-DT ability-NNof-IN R-NN to-IN.

Because the relations rst-background{circumflex over ( )}rst-enablementdiffer, the RST relation part is empty. The expressions that are theverbal definitions of respective RST relations are then generalized. Forexample, for each word or a placeholder for a word such as an agent,this word (with its POS) is retained if the word the same in each inputphrase or remove the word if the word is different between thesephrases. The resultant expression can be interpreted as a common meaningbetween the definitions of two different RST relations, obtainedformally.

Two arcs between the question and the answer depicted in FIG. 14 showthe generalization instance based on the RST relation “RST-contrast”.For example, “I just had a baby” is a RST-contrast with “it does notlook like me,” and related to “husband to avoid contact” which is aRST-contrast with “have the basic legal and financial commitments.” Ascan be seen, the answer need not have to be similar to the verb phraseof the question but the rhetoric structure of the question and answerare similar. Not all phrases in the answer must match phrases inquestion. For example, the phrases that do not match have certainrhetoric relations with the phrases in the answer which are relevant tophrases in question.

Building a Communicative Discourse Tree

FIG. 15 illustrates an exemplary process for building a communicativediscourse tree in accordance with an aspect. Rhetoric classificationapplication 102 can implement process 1500. As discussed, communicativediscourse trees enable improved search engine results.

At block 1501, process 1500 involves accessing a sentence includingfragments. At least one fragment includes a verb and words and each wordincludes a role of the words within the fragment, and each fragment isan elementary discourse unit. For example, rhetoric classificationapplication 102 accesses a sentence such as “Rebels, the self-proclaimedDonetsk People's Republic, deny that they controlled the territory fromwhich the missile was allegedly fired” as described with respect to FIG.13 .

Continuing the example, rhetoric classification application 102determines that the sentence includes several fragments. For example, afirst fragment is “rebels . . . deny.” A second fragment is “that theycontrolled the territory.” A third fragment is “from which the missilewas allegedly fired.” Each fragment includes a verb, for example, “deny”for the first fragment and “controlled” for the second fragment.Although, a fragment need not include a verb.

At block 1502, process 1500 involves generating a discourse tree thatrepresents rhetorical relationships between the sentence fragments. Thediscourse tree including nodes, each nonterminal node representing arhetorical relationship between two of the sentence fragments and eachterminal node of the nodes of the discourse tree is associated with oneof the sentence fragments.

Continuing the example, rhetoric classification application 102generates a discourse tree as shown in FIG. 13 . For example, the thirdfragment, “from which the missile was allegedly fired” elaborates on“that they controlled the territory.” The second and third fragmentstogether relate to attribution of what happened, i.e., the attack cannothave been the rebels because they do not control the territory.

At block 1503, process 1500 involves accessing multiple verb signatures.For example, rhetoric classification application 102 accesses a list ofverbs, e.g., from VerbNet. Each verb matches or is related to the verbof the fragment. For example, the for the first fragment, the verb is“deny.” Accordingly, rhetoric classification application 102 accesses alist of verb signatures that relate to the verb deny.

As discussed, each verb signature includes the verb of the fragment andone or more of thematic roles. For example, a signature includes one ormore of noun phrase (NP), noun (N), communicative action (V), verbphrase (VP), or adverb (ADV). The thematic roles describing therelationship between the verb and related words. For example “theteacher amused the children” has a different signature from “smallchildren amuse quickly.” For the first fragment, the verb “deny,”rhetoric classification application 102 accesses a list of frames, orverb signatures for verbs that match “deny.” The list is “NP V NP to beNP,” “NP V that S” and “NP V NP.”

Each verb signature includes thematic roles. A thematic role refers tothe role of the verb in the sentence fragment. Rhetoric classificationapplication 102 determines the thematic roles in each verb signature.Example thematic roles include actor, agent, asset, attribute,beneficiary, cause, location destination source, destination, source,location, experiencer, extent, instrument, material and product,material, product, patient, predicate, recipient, stimulus, theme, time,or topic.

At block 1504, process 1500 involves determining, for each verbsignature of the verb signatures, a number of thematic roles of therespective signature that match a role of a word in the fragment. Forthe first fragment, rhetorical classification application 102 determinesthat the verb “deny” has only three roles, “agent”, “verb” and “theme.”

At block 1505, process 1500 involves selecting a particular verbsignature from the verb signatures based on the particular verbsignature having a highest number of matches. For example, referringagain to FIG. 13 , deny in the first fragment “the rebels deny . . .that they control the territory” is matched to verb signature deny “NP VNP”, and “control” is matched to control (rebel, territory). Verbsignatures are nested, resulting in a nested signature of “deny(rebel,control(rebel, territory)).”

FIG. 16 illustrates a discourse tree and scenario graph in accordancewith an aspect. FIG. 16 depicts discourse tree 1601 and scenario graph1602. Discourse tree 1601 corresponds to the following three sentences:

(1) I explained that my check bounced (I wrote it after I made adeposit). A customer service representative accepted that it usuallytakes some time to process the deposit.

(2) I reminded that I was unfairly charged an overdraft fee a month agoin a similar situation. They denied that it was unfair because theoverdraft fee was disclosed in my account information.

(3) I disagreed with their fee and wanted this fee deposited back to myaccount. They explained that nothing can be done at this point and thatI need to look into the account rules closer.

As can be seen by the discourse tree in FIG. 16 , determining whetherthe text represents an interaction or a description can be hard tojudge. Hence, by analyzing the arcs of communicative actions of a parsethicket, implicit similarities between texts can be found. For example,in general terms:

(1) one communicative actions from with its subject from a first treeagainst another communicative action with its subject from a second tree(communicative action arc is not used).

(2) a pair of communicative actions with their subjects from a firsttree against another pair of communicative actions from a second tree(communicative action arcs are used).

For example, in the previous example, the generalization ofcheating(husband, wife, another lady){circumflex over ( )}avoid(husband,contact(husband, another lady)) provides uscommunicative_action(husband, *) which introduces a constraint on A inthe form that if a given agent (=husband) is mentioned as a subject ofCA in Q, he(she) should also be a subject of (possibly, another) CA inA.

To handle meaning of words expressing the subjects of CAs, a word can beapplied to a vector model such as the “word2vector” model. Morespecifically, to compute generalization between the subjects ofcommunicative actions, the following rule can be used: ifsubject1=subject2, subject1{circumflex over ( )}subject2=<subject1,POS(subject1), 1>. Here subject remains and score is 1. Otherwise, ifthe subjects have the same part-of-speech (POS), thensubject1{circumflex over ( )}subject2=<*, POS(subject1),word2vecDistance(subject1{circumflex over ( )}subject2)>. ‘*’ denotesthat lemma is a placeholder, and the score is a word2vec distancebetween these words. If POS is different, generalization is an emptytuple and may not be further generalized.

Nearest Neighbor Graph-Based Classification

Once a CDT is built, in order to identify an argument in text, rhetoricclassification application 102 compute the similarity compared to CDTsfor the positive class and verify that it is lower to the set of CDTsfor its negative class. Similarity between CDT is defined by means ofmaximal common sub-CDTs.

In an example, an ordered set G of CDTs(V,E) with vertex- andedge-labels from the sets (

, ≤) and (κ_(E), ≤) is constructed. A labeled CDT Γ from G is a pair ofpairs of the form ((V,l),(E,b)), where V is a set of vertices, E is aset of edges, l: V→

is a function assigning labels to vertices, and b: E→

is a function assigning labels to edges. Isomorphic trees with identicallabeling are not distinguished.

The order is defined as follows: For two CDTs Γ₁:=((V₁,l₁),(E₁,b₁)) andΓ₂:=((V₂,l₂),(E₂,b₂)) from G, then that Γ₁ dominates Γ₂ or Γ₂≤Γ₁ (or Γ₂is a sub-CDT of Γ₁) if there exists a one-to-one mapping φ: V₂→V₁ suchthat it (1) respects edges: (v,w)□E₂⇒(φ(v), φ(w))∈E₁, and (2) fits underlabels: l₂(v)≤l₁(φ(v)), (v,w)∈E₂⇒b₂(v,w)≤b₁(φ(v), φ(w)).

This definition takes into account the calculation of similarity(“weakening”) of labels of matched vertices when passing from the“larger” CDT G₁ to “smaller” CDT G₂.

Now, similarity CDT Z of a pair of CDTs X and Y, denoted by X{circumflexover ( )}Y=Z, is the set of all inclusion-maximal common sub-CDTs of Xand Y, each of them satisfying the following additional conditions (1)to be matched, two vertices from CDTs X and Y must denote the same RSTrelation; and (2) each common sub-CDT from Z contains at least onecommunicative action with the same VerbNet signature as in X and Y.

This definition is easily extended to finding generalizations of severalgraphs. The subsumption order □ on pairs of graph sets X and Y isnaturally defined as X□Y:=X□Y=X.

FIG. 17 illustrates a maximal common sub-communicative discourse tree inaccordance with an aspect. FIG. 17 includes communicative sub-discoursetree 1700. Communicative sub-discourse tree 1700 is inverted and thelabels of arcs are generalized: Communicative action site( ) isgeneralized with communicative action say( ). The first (agent) argumentof the former CA committee is generalized with the first argument of thelatter CA Dutch. The same operation is applied to the second argumentsfor this pair of CAs: investigator{circumflex over ( )}evidence.

CDT U belongs to a positive class such that (1) U is similar to (has anonempty common sub-CDT) with a positive example R⁺ and (2) for anynegative example R⁻, if U is similar to R⁻ (i.e., U□R⁻≠Ø) thenU*R⁻μU*R⁺.

This condition introduces the measure of similarity and says that to beassigned to a class, the similarity between the unknown CDT U and theclosest CDT from the positive class should be higher than the similaritybetween U and each negative example. Condition 2 implies that there is apositive example R⁺ such that for no R⁻ one has U□R⁺□R⁻, i.e., there isno counterexample to this generalization of positive examples.

Thicket Kernel Learning for CDT

Tree Kernel learning for strings, parse trees and parse thickets is awell-established research area these days. The parse tree kernel countsthe number of common sub-trees as the discourse similarity measurebetween two instances. Tree kernel has been defined for DT by Joty,Shafiq and A. Moschitti. Discriminative Reranking of Discourse ParsesUsing Tree Kernels. Proceedings of EMNLP. (2014). See also Wang, W., Su,J., & Tan, C. L. (2010). Kernel Based Discourse Relation Recognitionwith Temporal Ordering Information. In Proceedings of the 48th AnnualMeeting of the Association for Computational Linguistics. (using thespecial form of tree kernels for discourse relation recognition). Athicket kernel is defined for a CDT by augmenting a DT kernel by theinformation on communicative actions.

A CDT can be represented by a vector V of integer counts of eachsub-tree type (without taking into account its ancestors):

V (T)=(# of subtrees of type 1, . . . , # of subtrees of type I, . . . ,# of subtrees of type n). This results in a very high dimensionalitysince the number of different sub-trees is exponential in its size.Thus, it is computational infeasible to directly use the feature vectorØ(T). To solve the computational issue, a tree kernel function isintroduced to calculate the dot product between the above highdimensional vectors efficiently. Given two tree segments CDT1 and CDT2,the tree kernel function is defined:K(CDT1,CDT2)=<V(CDT1),V(CDT2)>=ΣiV(CDT1)[i], V(CDT2)[i]=Σn1Σn2 ΣiIi(n1)*Ii(n2) wheren1∈N1, n2∈N2 where N1 and N2 are the sets of all nodes in CDT1 and CDT2,respectively;Ii (n) is the indicator function.Ii (n)={1 iff a subtree of type i occurs with root at node; 0otherwise}. K (CDT1, CDT2) is an instance of convolution kernels overtree structures (Collins and Duffy, 2002) and can be computed byrecursive definitions:Δ(n1,n2)=ΣI Ii(n1)*Ii(n2)Δ (n1, n2)=0 if n1 and n2 are assigned the same POS tag or theirchildren are different subtrees.Otherwise, if both n1 and n2 are POS tags (are pre-terminal nodes) thenΔ (n1, n2)=1×λ;Otherwise, Δ (n1, n2)=λΠ_(j=1) ^(nc(n1))(1+Δ (ch(n1, j), ch(n2, j)))where ch(n,j) is the jth child of node n, nc(n₁) is the number of thechildren of n₁, and λ (0<λ<1) is the decay factor in order to make thekernel value less variable with respect to the sub-tree sizes. Inaddition, the recursive rule (3) holds because given two nodes with thesame children, one can construct common sub-trees using these childrenand common sub-trees of further offspring. The parse tree kernel countsthe number of common sub-trees as the syntactic similarity measurebetween two instances.

FIG. 18 illustrates a tree in a kernel learning format for acommunicative discourse tree in accordance with an aspect.

The terms for Communicative Actions as labels are converted into treeswhich are added to respective nodes for RST relations. For texts forEDUs as labels for terminal nodes only the phrase structure is retained.The terminal nodes are labeled with the sequence of phrase types insteadof parse tree fragments.

If there is a rhetoric relation arc from a node X to a terminal EDU nodeY with label A(B, C(D)), then the subtree A−B→(C−D) is appended to X.

Detecting a Request for an Explanation

As discussed, users often desire an explanation for a decision taken bya computing system that is operating a machine learning model. Withrespect to machine learning models, often, best classification accuracyis typically achieved by black-box machine learning models such asSupport Vector Machine, neural networks or random forests, orcomplicated ensembles of all of these. These systems are referred to asblack-boxes and their drawbacks are frequently cited since their innerworkings are really hard to understand. They do not usually provide aclear explanation of the reasons they made a certain decision orprediction; instead, they just output a probability associated with aprediction. On the other hand, machine learning methods whosepredictions are easy to understand and interpret frequently have limitedpredictive capacity (inductive inference, linear regression, a singledecision tree) or are inflexible and computationally cumbersome, such asexplicit graphical models. These methods usually require less data totrain from.

FIG. 19 illustrates an example of an electronic communication session inaccordance with an aspect. FIG. 19 illustrates communication session1900, which includes electronic message 1901 from user 1, electronicmessage 1902 from a first commenter, and electronic message 1903 from asecond commenter. As illustrated by electronic message 1901, a customerof financial services is appalled when he travels and his credit cardsare canceled without an obvious reason. The customer explains what hadhappened in the message thread, e.g., via social media. As can be seenfrom electronic messages 1902 and 1903, the user's friends stronglysupport his case again the bank. Not only did the bank make an error inits decision, according to what the friends write, but the bank is alsois unable to rectify the error and communicate the error properly. Ifthis bank used a decision making system with explainability, a givencause of the decision may exist. Once it is established that this causedoes not hold, the bank is expected to be capable of reverting itsdecision efficiently and retaining the customer.

FIG. 20 illustrates an example of an electronic translation of a phrasein accordance with an aspect. FIG. 20 includes translator 2000, whichincludes input 2001, output 2002, output 2003, and input 2004.Translator 2000 can use machine learning. The translator translates theterm “coil spring” (in Russian) into “spring spring.” This example showsproblem in the simplest case of translation where a meaning of two wordsneeds to be combined. A simple meta-reasoning system, a basic grammarchecking component or an entity lookup would prevent this translationerror under appropriate compartmental machine learning architecture withexplainability. However, a black-box implementation of machinetranslation breaks even in simple cases like this. Inverse translationis obviously flawed as well, as depicted by input 2004 and output 2003.

FIG. 21 illustrates an example of a search result of a phrase inaccordance with an aspect. FIG. 21 depicts search engine 2100, whichincludes search term 2101, output 2102, and button 2103. Output 2102shows a description for the entity. Search engine is another applicationarea for machine learning in which a relevance score is a majorcriterion to show certain search results. Having a highest relevancescore does not provide an explanation that the results are indeedrelevant. Typical relevance score such as TF*IDF is hardlyinterpretable; search highlighting features are helpful but the searchengine needs to be able to explain why it ignored certain keywords likenon-sufficient funds. A better phrase handling would also help: thesystem should recognize the whole expression stating “non-sufficientfunds fee” and if the expression does not occur in the search results,the system should explain the expression.

Using a Machine Learning Model to Determine a Request for Explanation

By solely relying on keywords, using keyword rules is insufficient todetect an implicit request to explain. Hence a machine learning approachwith an adequate training dataset is beneficial. A training set includestext with a request to explain and text that does not include one. Whena request to explain is not explicitly mentioned, discourse-levelfeatures are helpful. Accordingly, aspects use communicative discoursetrees as a means to represent discourse features associated withaffective argumentation.

FIG. 22 illustrates an exemplary process used to determine a presence ofa request for explanation in text in accordance with an aspect. Fordiscussion purposes, FIG. 22 is discussed in conjunction with FIG. 23 .

FIG. 23 illustrates an example of a linguistic representation of text inaccordance with an aspect. FIG. 23 shows representation 2300, whichincludes sentences 2301 and 2302 and rhetoric relations 2310-2314. Thistext was derived from a web-based answer for a question that requires acomplex response beyond “do this and do that” but rather demands a fullrecommendation with explanation: “I just had a baby and it looks morelike the husband I had my baby with. However it does not look like me atall and I am scared that he was cheating on me with another lady and Ihad her kid.” As can be seen, sentence 2301 is ““I just had a baby andit looks more like the husband I had my baby with” and sentence 2302 is“However it does not look like me at all and I am scared that he wascheating on me with another lady and I had her kid.”

As can be seen, FIG. 23 includes much richer information than just acombination of parse trees for individual sentences would. Navigationthrough this graph along the edges for syntactic relations as well asarcs for discourse relations allows to transform a given parse thicketinto semantically equivalent forms to cover a broader spectrum ofpossibilities to express a request to explain. To form a complete formalrepresentation of a paragraph, rhetoric classification application 102uncovers as many links as possible. As can be seen, each of thediscourse arcs produces a pair of thicket phrases that can be apotential match with an expression for explainability request.

A request for an explanation can be indicated in by a communicativediscourse tree in several different ways such as by a question or by astatement. For example, an elaboration relation in a question indicatesthat the author is stating something (the nucleus) and is asking aquestion about a related topic (the satellite). As such, the author maynot be particularly confident about his question. Consequently, rhetoricagreement application 102 can assume that the author has made a requestfor explanation and can then provide as much details as possible. Arequest for an explanation can also be indicated by a statement (asopposed to a question). Text that includes a rhetoric relation of“cause,” “contrast,” or “purpose,” “antithesis,” “concession,”“evaluation,” “evidence,” “interpretation,” “justification,”“motivation,” “non-volitional,” or “volitional” may include a requestfor an explanation.

More specifically, the chain of rhetoric relations 2310-2314 includesRST-elaboration (default), RST-sequence and RST-contrast indicate that aquestion is not just enumeration of topics and constraints for anexpected answer (that can be done by RST-elaboration only). Such a chainindicates that a conflict (an expression that something occurs incontrast to something else) is outlined in a question, so an answershould necessarily include an explanation.

At block 2201, process 2200 involves accessing text including fragments.Rhetoric classification application 102 can access text from differentsources such as input text 130 or Internet-based sources such as chat,Twitter, etc. Text can consist of fragments, sentences, paragraphs, orlonger amounts. For example, FIG. 23 comprises fragments (elementarydiscourse units) “I just had a baby,” and “it looks more like thehusband I had my baby with,” and so on.

At block 2202, process 2200 involves creating a communicative discoursetree from the text. At block 2202, process 2200 performs substantiallysimilar operations as in blocks 1501-1504 of process 1500. For example,rhetoric classification application 102 creates a discourse tree fromthe text and creates a communicative discourse tree from the discoursetree.

At block 2203, process 2200 involves determining whether thecommunicative discourse tree includes a request for an explanation byapplying a classification model trained to detect a request for anexplanation to the communicative discourse tree. As further discussed, aclassification model such as rhetoric agreement classifier 120 can betrained to make such classifications. The classification model can usedifferent learning approaches. For example, the classification model canuse a support vector machine with tree kernel learning. Additionally,the classification model can use nearest neighbor learning of maximalcommon sub-trees. An exemplary process is described with respect to FIG.25 .

As an example, rhetoric classification application 102 can use machinelearning to determine similarities between the communicative discoursetree identified at block 2203 and one or more communicative discoursetrees from a training set of communicative discourse trees. The trainingset of communicative discourse trees can be provided to rhetoricagreement classifier 120 during a training process. The positive setincludes communicative discourse trees representing text containing arequest for an explanation and the negative set includes communicativediscourse trees representing text without a request for an explanation.

Rhetoric agreement classifier 120 selects an additional communicativediscourse tree from the one or more communicative discourse trees basedon the additional communicative discourse tree having a highest numberof similarities with the communicative discourse tree. Based on asimilarity or difference between the communicative discourse tree andthe additional communicative discourse tree, rhetoric agreementclassifier 120 identifies whether the communicative discourse tree isfrom a positive set or a negative set by applying a classification modelto the communicative discourse tree. Based on the similarity, rhetoricagreement classifier 120 determines whether the text contains a requestfor an explanation.

For example, if the communicative discourse tree and an additionalcommunicative discourse tree in the positive set are above a thresholdin similarity, then rhetoric agreement classifier 120 classifies thecommunicative discourse tree as positive and determines that the textincludes a request for an explanation. Conversely, if the communicativediscourse tree and an additional communicative discourse tree in thenegative set are above a threshold in similarity, then rhetoricagreement classifier 120 classifies the communicative discourse tree asnegative and determines that the text does not include a request forexplanation.

Creating a Training Dataset and Training a Classification Model

FIG. 24 illustrates an exemplary process used to generate training datato train a classification model to determine a presence of a request forexplanation in text in accordance with an aspect. Training can be basedon the communicative discourse tree having a highest number ofsimilarities with another communicative discourse tree. Each trainingdata set includes a set of training pairs. Training data 125 includescommunicative discourse trees that include a request for an explanationin a positive dataset and communicative discourse trees that do not arequest for an explanation in a negative dataset.

For the positive dataset, various domains with distinct acceptancecriteria are selected that indicate whether an answer or response issuitable for the question. For example, each training set can include acommunicative discourse tree that represents a request for anexplanation and another communicative discourse tree that a request foran explanation and expected level match between a candidatecommunicative discourse tree and each communicative discourse tree.Rhetoric classification application 102 identifies whether an additionalcommunicative discourse tree generated from particular additionaltraining data should be added to the positive set or negative set,thereby increasing the amount of training data available and therobustness of the classification model when trained with the trainingdata.

At block 2401, method 2400 involves accessing text including fragments.Rhetoric classification application 102 accesses training data 125. Atblock 2401, rhetoric classification application 102 performssubstantially similar operations as block 1501 of process 1500.

At block 2402, method 2400 involves creating a discourse tree from thetext. At block 2402, rhetoric classification application 102 performssubstantially similar operations as block 1502 of process 1500.

At block 2403, method 2400 involves matching each fragment that has averb to a verb signature, thereby creating a communicative discoursetree. At block 2403, rhetoric classification application 102 performssubstantially similar operations as block 1503 of process 1500.

At block 2404, method 2400 involves accessing a positive communicativediscourse tree from a positive set and a negative communicativediscourse tree from a negative set.

At block 2405, method 2400 involves identifying whether thecommunicative discourse tree is from a positive set or a negative set byapplying rhetoric agreement classifier 120 to the communicativediscourse tree. Rhetoric agreement classifier 120 determinessimilarities between the communicative discourse tree from communicativediscourse trees with which the classifier was trained.

At block 2406, method 2400 involves adding the communicative discoursetree to either the positive training set or the negative training setbased on the identifying, thereby increasing the training data set size.

FIG. 25 illustrates an exemplary process used to train a classificationmodel to determine a presence of a request for explanation in text inaccordance with an aspect. In an aspect, rhetoric classificationapplication 102 uses training data 125, e.g., generated by method 2400,to train rhetoric agreement classifier 120 to determine a presence of arequest for an explanation. By using an iterative process, rhetoricclassification application 102 provides a training pair to rhetoricagreement classifier 120 and receives, from the model, a level ofcomplementarity. Acceptance criteria can vary by application. Forexample, acceptance criteria may be low for community questionanswering, automated question answering, automated and manual customersupport systems, social network communications and writing byindividuals such as consumers about their experience with products, suchas reviews and complaints. RR acceptance criteria may be high inscientific texts, professional journalism, health and legal documents inthe form of FAQ, professional social networks such as “stackoverflow.”

At block 2501, method 2500 involves training the classification model byproviding one of a set of training pairs to the classification model.Each training pair includes a communicative discourse tree and anexpected strength of a request for an explanation. For example, acommunicative discourse tree representing text that, based on therhetoric relations in the communicative discourse tree, is very likelyto include a request for an explanation may have a high expectedstrength.

At block 2502, method 2500 involves receiving a strength of a requestfor explanation from the classification model. A small or trivialdifference between the expected strength and the classification strengthindicates that the classification model is making good classifications.At blocks 2501-2502, rhetoric classification application 102 performssubstantially similar operations as performed at block 2203 of process2000.

At block 2503, method 2500 involves calculating a loss function bycalculating a difference between the expected strength and theclassification strength. The loss function is used to optimize orimprove rhetoric agreement classifier 120. Rhetoric classificationapplication 102 calculates a loss function by determining a differencebetween the determined level of complementarity and an expected level ofcomplementarity for the particular training pair. In this manner, theloss function represents a difference between ideal and measured outputsof the classifier. With each iteration, the process improves theclassifier.

At block 2504, method 2500 involves adjusting internal parameters of theclassification model to minimize the loss function. Based on the lossfunction, rhetoric classification application 102 adjusts internalparameters of the classification model such that the loss function isminimized. The trained rhetoric agreement classifier 120 can be used inprocess 2200 to determine a request for explanation in text.

A Dataset for Tracking Explainability Intent

The purpose of this dataset is to obtain texts where authors do theirbest to bring their points across by employing all means to show thatthey (as customers) are right and their opponents (companies) are wrong(Galitsky et al 2009). Complainants are emotionally charged writers whodescribe problems they encountered with a financial service, lack ofclarity and transparency as this problem was communicated with customersupport personnel, and how they attempted to solve it. Raw complaintsare collected for a number of banks submitted over last few years. Fourhundred complaints are manually tagged with respect to perceivedcomplaint validity, proper argumentation, detectable misrepresentation,and whether request for explanation concerning the company's decisionoccurred. Judging by complaints, most complainants are in genuinedistress due to a strong deviation between what they expected from aservice, what they received, how this deviation was explained and howthe problem was communicated by a customer support. Most complaintauthors report incompetence, flawed policies, ignorance, lack of commonsense, inability to understand the reason behind the company's decision,indifference to customer needs and misrepresentation from the customerservice personnel. The authors are frequently confused, looking forcompany's explanation, seeking recommendation from other users andadvise others on avoiding particular financial service. The focus of acomplaint is a proof that the proponent is right and her opponent iswrong, suggested explanation for why the company decides to act in acertain way, a resolution proposal and a desired outcome.

Multiple argumentation patterns are used in complaints. The mostfrequent is a deviation from what has happened from what was expected,according to common sense. This pattern covers both valid and invalidargumentation. The second in popularity argumentation patterns cites thedifference between what has been promised (advertised, communicated) andwhat has been received or actually occurred. This pattern also mentionsthat the opponent does not play by the rules (valid pattern). A highnumber of complaints are explicitly saying that bank representatives arelying. Lying includes inconsistencies between the information providedby different bank agents, factual misrepresentation and carelesspromises (valid pattern).

Another reason complaints arise is due to rudeness of bank agents andcustomer service personnel. Customers cite rudeness in both cases, whenthe opponent point is valid or not (and complaint and argumentationvalidity is tagged accordingly). Even if there is neither financial lossor inconvenience the complainants disagree with everything a given bankdoes, if they been served rudely (invalid pattern).

Complainants cite their needs as reasons bank should behave in certainways. A popular argument is that since the government via taxpayersbailed out the banks, they should now favor the customers (invalidpattern). Complaint authors reveal shady practice of banks during thefinancial crisis of 2007, such as manipulating an order of transactionsto charge a highest possible amount of nonsufficient fund fees.Moreover, banks attempted to communicate this practice as a necessity toprocess a wide amount of checks. This is the most frequent topic ofcustomer complaints, so one can track a manifold of argumentationpatterns applied to this topic.

For most frequent topics of complaints such as insufficient funds fee orunexpected interest rate rise on a credit card, this dataset providesmany distinct ways of argumentation that this fee is unfair. Therefore,this dataset allows for systematic exploration of the peculiartopic-independent clusters of argumentation patterns such as a requestto explain why certain decision was made. Unlike professional writing inlegal and political domains, authentic writing of complaining users havea simple motivational structure, a transparency of their purpose andoccurs in a fixed domain and context. Arguments play a critical rule forthe well-being of the authors, subject to an unfair charge of a largeamount of money or eviction from home. Therefore, the authors attempt toprovide as strong argumentation as possible to back up their claims andstrengthen their case.

The tag in this dataset used in the current study, request forexplanation, is related to the whole text of complaint, not a paragraph.Three annotators worked with this dataset, and inter-annotator agreementexceeds 80%.

Evaluation of Recognition Accuracy and Assessment of the Proportion ofRequest to Explain

Once we developed our algorithm for explanation request detection, wewant to train it, test it and verify how consistent its results areacross the domains. We also test how recognition accuracy varies forcases of different complexity.

Evidence # Criteria P R F1 Imperative 44 Keywords: explain, 92 94 93.0expression with clarify, make clear, communicative why did they act-action explain VP, why was it Double, triple+ 67 Multiple rhetoric 86 8384.5 implicit mention relation of contrast, attribution, sequence, causeSingle implicit 115 A pair of rhetoric 76 80 77.9 mention relationchains for contrast and cause

Detection accuracy for explanation request for different types ofevidence is shown in Table 1. We consider simpler cases where thedetection occurs based on phrases, in the top row. Typical expressionshere have an imperative form such as pleaseexplain/clarify/motivate/comment. Also, there are templates here such asyou did this but I expected that . . . you told me this but I receivedthat.

The middle row contains the data on higher evidence implicit explanationrequest case, where multiple fragments of DTs indicate the class.Finally, in the bottom row, we present the case of the lower confidencefor a single occurrence of a DT associated with an explanation request.The second column shows the counts of complaints per case. The thirdcolumn gives examples of expressions (which include keywords and phrasetypes) and rhetoric relations which serves as criteria for implicitexplanation request. Fourth, fifth and sixth columns presents thedetection rates where the complaints for a given case is mixed with ahundred of complaints without explanation request.

Recognition accuracies, bank-specific topics of complaints and anoverall proportion of the complaints with explanation request are shownin Table 2. We used 200 complaints for each bank to assess therecognition accuracies for explanation request (ER). One can observethat 82±3% is a reasonable estimate for recognition accuracy forexplanation request. The last column shows that taking into account <20%error rate in explanation request recognition, 25±4% is an adequateestimate of complaints requiring explainability in implicit or explicitform, given the set of 800 complaints.

Main topics of ER Source # complaints P R F1 rate Bank of 200 NSF,credit card interest 82 84 83.0 28.5 America rate raise Chase 200 NSF,foreclosure, 80 82 81.0 25.8 Bank unexpected card cancellation Citibank200 Foreclosure, mortgage 79 83 81.0 23.8 application, refinancing,American 200 Card application, NSF, 83 82 82.5 27.0 Express late payment

Finally, we ran our explanation request detection engine against the setof 10000 complaints scraped from PlanetFeedback.com and observed that27% of complainants require explainability from companies, being theircustomer. There is a single complaint per author and a number ofexceptions is unnoticeable. The conclusion is that since almost a thirdof customers strongly demand and rely on explainability of thecompanies' decisions these customers are affected. Hence the companiesneed to employ ML algorithms with explainability feature.

This explainability feature is more important than the recognitionaccuracy for the customers, who understand that all businesses makeerrors. Typically, when a company makes a wrong decision via ML but thenrectifies it efficiently, a complaint does not arise. The most importantmeans for customer retention is then properly communicating with themboth correct and possibly erroneous customer decisions (notquantitatively evaluated in this study).

Exemplary Computing Devices

FIG. 26 depicts a simplified diagram of a distributed system 2600 forimplementing one of the aspects. In the illustrated aspect, distributedsystem 2600 includes one or more client computing devices 2602, 2604,2606, and 2608, which are configured to execute and operate a clientapplication such as a web browser, proprietary client (e.g., OracleForms), or the like over one or more network(s) 2610. Server 2612 may becommunicatively coupled with remote client computing devices 2602, 2604,2606, and 2608 via network 2610.

In various aspects, server 2612 may be adapted to run one or moreservices or software applications provided by one or more of thecomponents of the system. The services or software applications caninclude nonvirtual and virtual environments. Virtual environments caninclude those used for virtual events, tradeshows, simulators,classrooms, shopping exchanges, and enterprises, whether two- orthree-dimensional (3D) representations, page-based logical environments,or otherwise. In some aspects, these services may be offered asweb-based or cloud services or under a Software as a Service (SaaS)model to the users of client computing devices 2602, 2604, 2606, and/or2608. Users operating client computing devices 2602, 2604, 2606, and/or2608 may in turn utilize one or more client applications to interactwith server 2612 to utilize the services provided by these components.

In the configuration depicted in the figure, the software components2618, 2620 and 2623 of system 2600 are shown as being implemented onserver 812. In other aspects, one or more of the components of system2600 and/or the services provided by these components may also beimplemented by one or more of the client computing devices 2602, 2604,2606, and/or 2608. Users operating the client computing devices may thenutilize one or more client applications to use the services provided bythese components. These components may be implemented in hardware,firmware, software, or combinations thereof. It should be appreciatedthat various different system configurations are possible, which may bedifferent from distributed system 2600. The aspect shown in the figureis thus one example of a distributed system for implementing an aspectsystem and is not intended to be limiting.

Client computing devices 2602, 2604, 2606, and/or 2608 may be portablehandheld devices (e.g., an iPhone®, cellular telephone, an iPad®,computing tablet, a personal digital assistant (PDA)) or wearabledevices (e.g., a Google Glass® head mounted display), running softwaresuch as Microsoft Windows Mobile®, and/or a variety of mobile operatingsystems such as iOS, Windows Phone, Android, BlackBerry 10, Palm OS, andthe like, and being Internet, e-mail, short message service (SMS),Blackberry®, or other communication protocol enabled. The clientcomputing devices can be general purpose personal computers including,by way of example, personal computers and/or laptop computers runningvarious versions of Microsoft Windows®, Apple Macintosh®, and/or Linuxoperating systems. The client computing devices can be workstationcomputers running any of a variety of commercially-available UNIX® orUNIX-like operating systems, including without limitation the variety ofGNU/Linux operating systems, such as for example, Google Chrome OS.Alternatively, or in addition, client computing devices 2602, 2604,2606, and 2608 may be any other electronic device, such as a thin-clientcomputer, an Internet-enabled gaming system (e.g., a Microsoft Xboxgaming console with or without a Kinect® gesture input device), and/or apersonal messaging device, capable of communicating over network(s)2610.

Although exemplary distributed system 2600 is shown with four clientcomputing devices, any number of client computing devices may besupported. Other devices, such as devices with sensors, etc., mayinteract with server 2612.

Network(s) 2610 in distributed system 2600 may be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-availableprotocols, including without limitation TCP/IP (transmission controlprotocol/Internet protocol), SNA (systems network architecture), IPX(Internet packet exchange), AppleTalk, and the like. Merely by way ofexample, network(s) 2610 can be a local area network (LAN), such as onebased on Ethernet, Token-Ring and/or the like. Network(s) 2610 can be awide-area network and the Internet. It can include a virtual network,including without limitation a virtual private network (VPN), anintranet, an extranet, a public switched telephone network (PSTN), aninfra-red network, a wireless network (e.g., a network operating underany of the Institute of Electrical and Electronics (IEEE) 802.26 suiteof protocols, Bluetooth®, and/or any other wireless protocol); and/orany combination of these and/or other networks.

Server 2612 may be composed of one or more general purpose computers,specialized server computers (including, by way of example, PC (personalcomputer) servers, UNIX® servers, mid-range servers, mainframecomputers, rack-mounted servers, etc.), server farms, server clusters,or any other appropriate arrangement and/or combination. Server 2612 caninclude one or more virtual machines running virtual operating systems,or other computing architectures involving virtualization. One or moreflexible pools of logical storage devices can be virtualized to maintainvirtual storage devices for the server. Virtual networks can becontrolled by server 2612 using software defined networking. In variousaspects, server 2612 may be adapted to run one or more services orsoftware applications described in the foregoing disclosure. Forexample, server 2612 may correspond to a server for performingprocessing described above according to an aspect of the presentdisclosure.

Server 2612 may run an operating system including any of those discussedabove, as well as any commercially available server operating system.Server 2612 may also run any of a variety of additional serverapplications and/or mid-tier applications, including HTTP (hypertexttransport protocol) servers, FTP (file transfer protocol) servers, CGI(common gateway interface) servers, JAVA® servers, database servers, andthe like. Exemplary database servers include without limitation thosecommercially available from Oracle, Microsoft, Sybase, IBM(International Business Machines), and the like.

In some implementations, server 2612 may include one or moreapplications to analyze and consolidate data feeds and/or event updatesreceived from users of client computing devices 2602, 2604, 2606, and2608. As an example, data feeds and/or event updates may include, butare not limited to, Twitter® feeds, Facebook® updates or real-timeupdates received from one or more third party information sources andcontinuous data streams, which may include real-time events related tosensor data applications, financial tickers, network performancemeasuring tools (e.g., network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like. Server 2612 may also include one or moreapplications to display the data feeds and/or real-time events via oneor more display devices of client computing devices 2602, 2604, 2606,and 2608.

Distributed system 2600 may also include one or more databases 2614 and2616. Databases 2614 and 2616 may reside in a variety of locations. Byway of example, one or more of databases 2614 and 2616 may reside on anon-transitory storage medium local to (and/or resident in) server 2612.Alternatively, databases 2614 and 2616 may be remote from server 2612and in communication with server 2612 via a network-based or dedicatedconnection. In one set of aspects, databases 2614 and 2616 may reside ina storage-area network (SAN). Similarly, any necessary files forperforming the functions attributed to server 2612 may be stored locallyon server 2612 and/or remotely, as appropriate. In one set of aspects,databases 2614 and 2616 may include relational databases, such asdatabases provided by Oracle, that are adapted to store, update, andretrieve data in response to SQL-formatted commands.

FIG. 27 is a simplified block diagram of one or more components of asystem environment 2700 by which services provided by one or morecomponents of an aspect system may be offered as cloud services inaccordance with an aspect of the present disclosure. In the illustratedaspect, system environment 2700 includes one or more client computingdevices 2704, 2706, and 2708 that may be used by users to interact witha cloud infrastructure system 2702 that provides cloud services. Theclient computing devices may be configured to operate a clientapplication such as a web browser, a proprietary client application(e.g., Oracle Forms), or some other application, which may be used by auser of the client computing device to interact with cloudinfrastructure system 2702 to use services provided by cloudinfrastructure system 2702.

It should be appreciated that cloud infrastructure system 2702 depictedin the figure may have other components than those depicted. Further,the aspect shown in the figure is only one example of a cloudinfrastructure system that may incorporate an aspect of the invention.In some other aspects, cloud infrastructure system 2702 may have more orfewer components than shown in the figure, may combine two or morecomponents, or may have a different configuration or arrangement ofcomponents.

Client computing devices 2704, 2706, and 2708 may be devices similar tothose described above for 2602, 2604, 2606, and 2608.

Although exemplary system environment 2700 is shown with three clientcomputing devices, any number of client computing devices may besupported. Other devices such as devices with sensors, etc. may interactwith cloud infrastructure system 2602.

Network(s) 2710 may facilitate communications and exchange of databetween clients 2704, 2706, and 2708 and cloud infrastructure system2702. Each network may be any type of network familiar to those skilledin the art that can support data communications using any of a varietyof commercially-available protocols, including those described above fornetwork(s) 2710.

Cloud infrastructure system 2702 may comprise one or more computersand/or servers that may include those described above for server 2712.

In certain aspects, services provided by the cloud infrastructure systemmay include a host of services that are made available to users of thecloud infrastructure system on demand, such as online data storage andbackup solutions, Web-based e-mail services, hosted office suites anddocument collaboration services, database processing, managed technicalsupport services, and the like. Services provided by the cloudinfrastructure system can dynamically scale to meet the needs of itsusers. A specific instantiation of a service provided by cloudinfrastructure system is referred to herein as a “service instance.” Ingeneral, any service made available to a user via a communicationnetwork, such as the Internet, from a cloud service provider's system isreferred to as a “cloud service.” Typically, in a public cloudenvironment, servers and systems that make up the cloud serviceprovider's system are different from the customer's own on-premisesservers and systems. For example, a cloud service provider's system mayhost an application, and a user may, via a communication network such asthe Internet, on demand, order and use the application.

In some examples, a service in a computer network cloud infrastructuremay include protected computer network access to storage, a hosteddatabase, a hosted web server, a software application, or other serviceprovided by a cloud vendor to a user, or as otherwise known in the art.For example, a service can include password-protected access to remotestorage on the cloud through the Internet. As another example, a servicecan include a web service-based hosted relational database and ascript-language middleware engine for private use by a networkeddeveloper. As another example, a service can include access to an emailsoftware application hosted on a cloud vendor's web site.

In certain aspects, cloud infrastructure system 2702 may include a suiteof applications, middleware, and database service offerings that aredelivered to a customer in a self-service, subscription-based,elastically scalable, reliable, highly available, and secure manner. Anexample of such a cloud infrastructure system is the Oracle Public Cloudprovided by the present assignee.

Large volumes of data, sometimes referred to as big data, can be hostedand/or manipulated by the infrastructure system on many levels and atdifferent scales. Such data can include data sets that are so large andcomplex that it can be difficult to process using typical databasemanagement tools or traditional data processing applications. Forexample, terabytes of data may be difficult to store, retrieve, andprocess using personal computers or their rack-based counterparts. Suchsizes of data can be difficult to work with using most currentrelational database management systems and desktop statistics andvisualization packages. They can require massively parallel processingsoftware running thousands of server computers, beyond the structure ofcommonly used software tools, to capture, curate, manage, and processthe data within a tolerable elapsed time.

Extremely large data sets can be stored and manipulated by analysts andresearchers to visualize large amounts of data, detect trends, and/orotherwise interact with the data. Tens, hundreds, or thousands ofprocessors linked in parallel can act upon such data in order to presentit or simulate external forces on the data or what it represents. Thesedata sets can involve structured data, such as that organized in adatabase or otherwise according to a structured model, and/orunstructured data (e.g., emails, images, data blobs (binary largeobjects), web pages, complex event processing). By leveraging an abilityof an aspect to relatively quickly focus more (or fewer) computingresources upon an objective, the cloud infrastructure system may bebetter available to carry out tasks on large data sets based on demandfrom a business, government agency, research organization, privateindividual, group of like-minded individuals or organizations, or otherentity.

In various aspects, cloud infrastructure system 2702 may be adapted toautomatically provision, manage and track a customer's subscription toservices offered by cloud infrastructure system 2702. Cloudinfrastructure system 2702 may provide the cloud services via differentdeployment models. For example, services may be provided under a publiccloud model in which cloud infrastructure system 2702 is owned by anorganization selling cloud services (e.g., owned by Oracle) and theservices are made available to the general public or different industryenterprises. As another example, services may be provided under aprivate cloud model in which cloud infrastructure system 2702 isoperated solely for a single organization and may provide services forone or more entities within the organization. The cloud services mayalso be provided under a community cloud model in which cloudinfrastructure system 2702 and the services provided by cloudinfrastructure system 2702 are shared by several organizations in arelated community. The cloud services may also be provided under ahybrid cloud model, which is a combination of two or more differentmodels.

In some aspects, the services provided by cloud infrastructure system2702 may include one or more services provided under Software as aService (SaaS) category, Platform as a Service (PaaS) category,Infrastructure as a Service (IaaS) category, or other categories ofservices including hybrid services. A customer, via a subscriptionorder, may order one or more services provided by cloud infrastructuresystem 2702. Cloud infrastructure system 2702 then performs processingto provide the services in the customer's subscription order.

In some aspects, the services provided by cloud infrastructure system2702 may include, without limitation, application services, platformservices and infrastructure services. In some examples, applicationservices may be provided by the cloud infrastructure system via a SaaSplatform. The SaaS platform may be configured to provide cloud servicesthat fall under the SaaS category. For example, the SaaS platform mayprovide capabilities to build and deliver a suite of on-demandapplications on an integrated development and deployment platform. TheSaaS platform may manage and control the underlying software andinfrastructure for providing the SaaS services. By utilizing theservices provided by the SaaS platform, customers can utilizeapplications executing on the cloud infrastructure system. Customers canacquire the application services without the need for customers topurchase separate licenses and support. Various different SaaS servicesmay be provided. Examples include, without limitation, services thatprovide solutions for sales performance management, enterpriseintegration, and business flexibility for large organizations.

In some aspects, platform services may be provided by the cloudinfrastructure system via a PaaS platform. The PaaS platform may beconfigured to provide cloud services that fall under the PaaS category.Examples of platform services may include without limitation servicesthat enable organizations (such as Oracle) to consolidate existingapplications on a shared, common architecture, as well as the ability tobuild new applications that leverage the shared services provided by theplatform. The PaaS platform may manage and control the underlyingsoftware and infrastructure for providing the PaaS services. Customerscan acquire the PaaS services provided by the cloud infrastructuresystem without the need for customers to purchase separate licenses andsupport. Examples of platform services include, without limitation,Oracle Java Cloud Service (JCS), Oracle Database Cloud Service (DBCS),and others.

By utilizing the services provided by the PaaS platform, customers canemploy programming languages and tools supported by the cloudinfrastructure system and also control the deployed services. In someaspects, platform services provided by the cloud infrastructure systemmay include database cloud services, middleware cloud services (e.g.,Oracle Fusion Middleware services), and Java cloud services. In oneaspect, database cloud services may support shared service deploymentmodels that enable organizations to pool database resources and offercustomers a Database as a Service in the form of a database cloud.Middleware cloud services may provide a platform for customers todevelop and deploy various business applications, and Java cloudservices may provide a platform for customers to deploy Javaapplications, in the cloud infrastructure system.

Various different infrastructure services may be provided by an IaaSplatform in the cloud infrastructure system. The infrastructure servicesfacilitate the management and control of the underlying computingresources, such as storage, networks, and other fundamental computingresources for customers utilizing services provided by the SaaS platformand the PaaS platform.

In certain aspects, cloud infrastructure system 2702 may also includeinfrastructure resources 2730 for providing the resources used toprovide various services to customers of the cloud infrastructuresystem. In one aspect, infrastructure resources 2730 may includepre-integrated and optimized combinations of hardware, such as servers,storage, and networking resources to execute the services provided bythe PaaS platform and the SaaS platform.

In some aspects, resources in cloud infrastructure system 2702 may beshared by multiple users and dynamically re-allocated per demand.Additionally, resources may be allocated to users in different timezones. For example, cloud infrastructure system 2730 may enable a firstset of users in a first time zone to utilize resources of the cloudinfrastructure system for a specified number of hours and then enablethe re-allocation of the same resources to another set of users locatedin a different time zone, thereby maximizing the utilization ofresources.

In certain aspects, a number of internal shared services 2732 may beprovided that are shared by different components or modules of cloudinfrastructure system 2702 and by the services provided by cloudinfrastructure system 2702. These internal shared services may include,without limitation, a security and identity service, an integrationservice, an enterprise repository service, an enterprise managerservice, a virus scanning and white list service, a high availability,backup and recovery service, service for enabling cloud support, anemail service, a notification service, a file transfer service, and thelike.

In certain aspects, cloud infrastructure system 2702 may providecomprehensive management of cloud services (e.g., SaaS, PaaS, and IaaSservices) in the cloud infrastructure system. In one aspect, cloudmanagement functionality may include capabilities for provisioning,managing and tracking a customer's subscription received by cloudinfrastructure system 2702, and the like.

In one aspect, as depicted in the figure, cloud management functionalitymay be provided by one or more modules, such as an order managementmodule 2620, an order orchestration module 2620, an order provisioningmodule 2624, an order management and monitoring module 2626, and anidentity management module 2628. These modules may include or beprovided using one or more computers and/or servers, which may begeneral purpose computers, specialized server computers, server farms,server clusters, or any other appropriate arrangement and/orcombination.

In exemplary operation 2634, a customer using a client device, such asclient device 2704, 2706 or 2708, may interact with cloud infrastructuresystem 2702 by requesting one or more services provided by cloudinfrastructure system 2702 and placing an order for a subscription forone or more services offered by cloud infrastructure system 2702. Incertain aspects, the customer may access a cloud User Interface (UI),cloud UI 2612, cloud UI 2614 and/or cloud UI 2616 and place asubscription order via these UIs. The order information received bycloud infrastructure system 2702 in response to the customer placing anorder may include information identifying the customer and one or moreservices offered by the cloud infrastructure system 2702 that thecustomer intends to subscribe to.

After an order has been placed by the customer, the order information isreceived via the cloud UIs, 2727, 2714 and/or 2716.

At operation 2636, the order is stored in order database 2718. Orderdatabase 2618 can be one of several databases operated by cloudinfrastructure system 2620 and operated in conjunction with other systemelements.

At operation 2638, the order information is forwarded to an ordermanagement module 2620. In some instances, order management module 2620may be configured to perform billing and accounting functions related tothe order, such as verifying the order, and upon verification, bookingthe order.

At operation 2640, information regarding the order is communicated to anorder orchestration module 2622. Order orchestration module 2622 mayutilize the order information to orchestrate the provisioning ofservices and resources for the order placed by the customer. In someinstances, order orchestration module 2622 may orchestrate theprovisioning of resources to support the subscribed services using theservices of order provisioning module 2624.

In certain aspects, order orchestration module 2622 enables themanagement of business processes associated with each order and appliesbusiness logic to determine whether an order should proceed toprovisioning. At operation 2642, upon receiving an order for a newsubscription, order orchestration module 2622 sends a request to orderprovisioning module 2624 to allocate resources and configure thoseresources needed to fulfill the subscription order. Order provisioningmodule 2624 enables the allocation of resources for the services orderedby the customer. Order provisioning module 2624 provides a level ofabstraction between the cloud services provided by cloud infrastructuresystem 2600 and the physical implementation layer that is used toprovision the resources for providing the requested services. Orderorchestration module 2622 may thus be isolated from implementationdetails, such as whether or not services and resources are actuallyprovisioned on the fly or pre-provisioned and only allocated/assignedupon request.

At operation 2642, once the services and resources are provisioned, anotification of the provided service may be sent to customers on clientdevices 2604, 2606 and/or 2608 by order provisioning module 2624 ofcloud infrastructure system 2602.

At operation 2646, the customer's subscription order may be managed andtracked by an order management and monitoring module 2626. In someinstances, order management and monitoring module 2626 may be configuredto collect usage statistics for the services in the subscription order,such as the amount of storage used, the amount data transferred, thenumber of users, and the amount of system up time and system down time.

In certain aspects, cloud infrastructure system 2700 may include anidentity management module 2628. Identity management module 2628 may beconfigured to provide identity services, such as access management andauthorization services in cloud infrastructure system 2700. In someaspects, identity management module 2628 may control information aboutcustomers who wish to utilize the services provided by cloudinfrastructure system 2702. Such information can include informationthat authenticates the identities of such customers and information thatdescribes which actions those customers are authorized to performrelative to various system resources (e.g., files, directories,applications, communication ports, memory segments, etc.) Identitymanagement module 2628 may also include the management of descriptiveinformation about each customer and about how and by whom thatdescriptive information can be accessed and modified.

FIG. 28 illustrates an exemplary computer system 2800, in which variousaspects of the present invention may be implemented. The system 2800 maybe used to implement any of the computer systems described above. Asshown in the figure, computer system 2800 includes a processing unit2804 that communicates with a number of peripheral subsystems via a bussubsystem 2802. These peripheral subsystems may include a processingacceleration unit 2806, an I/O subsystem 2808, a storage subsystem 2818and a communications subsystem 2825. Storage subsystem 2818 includestangible computer-readable storage media 2823 and a system memory 2810.

Bus subsystem 2802 provides a mechanism for letting the variouscomponents and subsystems of computer system 2800 communicate with eachother as intended. Although bus subsystem 2802 is shown schematically asa single bus, alternative aspects of the bus subsystem may utilizemultiple buses. Bus subsystem 2802 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Forexample, such architectures may include an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P2886.1standard.

Processing unit 2804, which can be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 2800. One or more processorsmay be included in processing unit 2804. These processors may includesingle core or multicore processors. In certain aspects, processing unit2804 may be implemented as one or more independent processing units 2832and/or 2834 with single or multicore processors included in eachprocessing unit. In other aspects, processing unit 2804 may also beimplemented as a quad-core processing unit formed by integrating twodual-core processors into a single chip.

In various aspects, processing unit 2804 can execute a variety ofprograms in response to program code and can maintain multipleconcurrently executing programs or processes. At any given time, some orall of the program code to be executed can be resident in processingunit(s) 2804 and/or in storage subsystem 2818. Through suitableprogramming, processing unit(s) 2804 can provide various functionalitiesdescribed above. Computer system 2800 may additionally include aprocessing acceleration unit 2806, which can include a digital signalprocessor (DSP), a special-purpose processor, and/or the like.

I/O subsystem 2808 may include user interface input devices and userinterface output devices. User interface input devices may include akeyboard, pointing devices such as a mouse or trackball, a touchpad ortouch screen incorporated into a display, a scroll wheel, a click wheel,a dial, a button, a switch, a keypad, audio input devices with voicecommand recognition systems, microphones, and other types of inputdevices. User interface input devices may include, for example, motionsensing and/or gesture recognition devices such as the Microsoft Kinect®motion sensor that enables users to control and interact with an inputdevice, such as the Microsoft Xbox® 280 game controller, through anatural user interface using gestures and spoken commands. Userinterface input devices may also include eye gesture recognition devicessuch as the Google Glass® blink detector that detects eye activity(e.g., ‘blinking’ while taking pictures and/or making a menu selection)from users and transforms the eye gestures as input into an input device(e.g., Google Glass®). Additionally, user interface input devices mayinclude voice recognition sensing devices that enable users to interactwith voice recognition systems (e.g., Siri® navigator), through voicecommands.

User interface input devices may also include, without limitation, threedimensional (3D) mice, joysticks or pointing sticks, gamepads andgraphic tablets, and audio/visual devices such as speakers, digitalcameras, digital camcorders, portable media players, webcams, imagescanners, fingerprint scanners, barcode reader 3D scanners, 3D printers,laser rangefinders, and eye gaze tracking devices. Additionally, userinterface input devices may include, for example, medical imaging inputdevices such as computed tomography, magnetic resonance imaging,position emission tomography, medical ultrasonography devices. Userinterface input devices may also include, for example, audio inputdevices such as MIDI keyboards, digital musical instruments and thelike.

User interface output devices may include a display subsystem, indicatorlights, or non-visual displays such as audio output devices, etc. Thedisplay subsystem may be a cathode ray tube (CRT), a flat-panel device,such as that using a liquid crystal display (LCD) or plasma display, aprojection device, a touch screen, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system2800 to a user or other computer. For example, user interface outputdevices may include, without limitation, a variety of display devicesthat visually convey text, graphics and audio/video information such asmonitors, printers, speakers, headphones, automotive navigation systems,plotters, voice output devices, and modems.

Computer system 2800 may comprise a storage subsystem 2818 thatcomprises software elements, shown as being currently located within asystem memory 2810. System memory 2810 may store program instructionsthat are loadable and executable on processing unit 2804, as well asdata generated during the execution of these programs.

Depending on the configuration and type of computer system 2800, systemmemory 2810 may be volatile (such as random access memory (RAM)) and/ornon-volatile (such as read-only memory (ROM), flash memory, etc.) TheRAM typically contains data and/or program modules that are immediatelyaccessible to and/or presently being operated and executed by processingunit 2804. In some implementations, system memory 2810 may includemultiple different types of memory, such as static random access memory(SRAM) or dynamic random access memory (DRAM). In some implementations,a basic input/output system (BIOS), containing the basic routines thathelp to transfer information between elements within computer system2800, such as during start-up, may typically be stored in the ROM. Byway of example, and not limitation, system memory 2810 also illustratesapplication programs 2812, which may include client applications, Webbrowsers, mid-tier applications, relational database management systems(RDBMS), etc., program data 2814, and an operating system 2816. By wayof example, operating system 2816 may include various versions ofMicrosoft Windows®, Apple Macintosh®, and/or Linux operating systems, avariety of commercially-available UNIX® or UNIX-like operating systems(including without limitation the variety of GNU/Linux operatingsystems, the Google Chrome® OS, and the like) and/or mobile operatingsystems such as iOS, Windows® Phone, Android® OS, BlackBerry® 10 OS, andPalm® OS operating systems.

Storage subsystem 2818 may also provide a tangible computer-readablestorage medium for storing the basic programming and data constructsthat provide the functionality of some aspects. Software (programs, codemodules, instructions) that when executed by a processor provide thefunctionality described above may be stored in storage subsystem 2818.These software modules or instructions may be executed by processingunit 2804. Storage subsystem 2818 may also provide a repository forstoring data used in accordance with the present invention.

Storage subsystem 2800 may also include a computer-readable storagemedia reader 2820 that can further be connected to computer-readablestorage media 2823. Together and, optionally, in combination with systemmemory 2810, computer-readable storage media 2823 may comprehensivelyrepresent remote, local, fixed, and/or removable storage devices plusstorage media for temporarily and/or more permanently containing,storing, transmitting, and retrieving computer-readable information.

Computer-readable storage media 2823 containing code, or portions ofcode, can also include any appropriate media known or used in the art,including storage media and communication media, such as but not limitedto, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible, non-transitorycomputer-readable storage media such as RAM, ROM, electronicallyerasable programmable ROM (EEPROM), flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD), or other opticalstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible computer readablemedia. When specified, this can also include nontangible, transitorycomputer-readable media, such as data signals, data transmissions, orany other medium which can be used to transmit the desired informationand which can be accessed by computing system 2800.

By way of example, computer-readable storage media 2823 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 2823 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 2823 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 2800.

Communications subsystem 2825 provides an interface to other computersystems and networks. Communications subsystem 2825 serves as aninterface for receiving data from and transmitting data to other systemsfrom computer system 2800. For example, communications subsystem 2825may enable computer system 2800 to connect to one or more devices viathe Internet. In some aspects, communications subsystem 2825 can includeradio frequency (RF) transceiver components for accessing wireless voiceand/or data networks (e.g., using cellular telephone technology,advanced data network technology, such as 3G, 4G or EDGE (enhanced datarates for global evolution), WiFi (IEEE 802.28 family standards, orother mobile communication technologies, or any combination thereof),global positioning system (GPS) receiver components, and/or othercomponents. In some aspects, communications subsystem 2825 can providewired network connectivity (e.g., Ethernet) in addition to or instead ofa wireless interface.

In some aspects, communications subsystem 2825 may also receive inputcommunication in the form of structured and/or unstructured data feeds2828, event streams 2828, event updates 2828, and the like on behalf ofone or more users who may use computer system 2800.

By way of example, communications subsystem 2825 may be configured toreceive unstructured data feeds 2828 in real-time from users of socialmedia networks and/or other communication services such as Twitter®feeds, Facebook® updates, web feeds such as Rich Site Summary (RSS)feeds, and/or real-time updates from one or more third party informationsources.

Additionally, communications subsystem 2825 may also be configured toreceive data in the form of continuous data streams, which may includeevent streams 2828 of real-time events and/or event updates 2828, thatmay be continuous or unbounded in nature with no explicit end. Examplesof applications that generate continuous data may include, for example,sensor data applications, financial tickers, network performancemeasuring tools (e.g. network monitoring and traffic managementapplications), clickstream analysis tools, automobile trafficmonitoring, and the like.

Communications subsystem 2825 may also be configured to output thestructured and/or unstructured data feeds 2828, event streams 2828,event updates 2828, and the like to one or more databases that may be incommunication with one or more streaming data source computers coupledto computer system 2800.

Computer system 2800 can be one of various types, including a handheldportable device (e.g., an iPhone® cellular phone, an iPad® computingtablet, a PDA), a wearable device (e.g., a Google Glass® head mounteddisplay), a PC, a workstation, a mainframe, a kiosk, a server rack, orany other data processing system.

Due to the ever-changing nature of computers and networks, thedescription of computer system 2800 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software (includingapplets), or a combination. Further, connection to other computingdevices, such as network input/output devices, may be employed. Based onthe disclosure and teachings provided herein, a person of ordinary skillin the art will appreciate other ways and/or methods to implement thevarious aspects.

In the foregoing specification, aspects of the invention are describedwith reference to specific aspects thereof, but those skilled in the artwill recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, aspects can be utilized in any numberof environments and applications beyond those described herein withoutdeparting from the broader spirit and scope of the specification. Thespecification and drawings are, accordingly, to be regarded asillustrative rather than restrictive.

What is claimed is:
 1. A non-transitory computer-readable storage mediumstoring computer-executable program instructions for detecting a requestfor explanation in text, wherein when executed by a processing device,the computer-executable program instructions cause the processing deviceto perform operations comprising: receiving text comprising fragments;creating a discourse tree from the text, wherein the discourse treecomprises a plurality of nodes, each nonterminal node representing arhetorical relationship between two of the fragments and each terminalnode of the nodes of the discourse tree is associated with one of thefragments; forming a communicative discourse tree that represents thetext by matching each fragment that has a verb to a verb signature;identifying that the text comprises a request for an explanation byapplying a classification model trained to detect a request for anexplanation to the communicative discourse tree; adjusting a responsebased on the identified request for explanation; and providing theadjusted response.
 2. The non-transitory computer-readable storagemedium of claim 1, wherein the matching comprises: accessing verbsignatures, wherein each verb signature comprises a verb of therespective fragment and a sequence of thematic roles, wherein eachthematic role describes a relationship between the verb and relatedwords; determining, for each verb signature of the verb signatures, athematic role that matches a role of a word in a respective fragment;selecting a particular verb signature from verb signatures based on theparticular verb signature comprising a highest number of matches; andassociating the particular verb signature with the respective fragment.3. The non-transitory computer-readable storage medium of claim 2,wherein associating the particular verb signature with the respectivefragment further comprises: identifying each of a thematic roles in theparticular verb signature; and matching, for each of the thematic rolesin the particular verb signature, a corresponding word in the respectivefragment to the thematic role.
 4. The non-transitory computer-readablestorage medium of claim 2, wherein (i) the classification model is asupport vector machine with tree kernel learning or (ii) theclassification model uses nearest neighbor learning of maximal commonsub-trees.
 5. The non-transitory computer-readable storage medium ofclaim 1, wherein applying the classification model further comprises:selecting an additional communicative discourse tree based on theadditional communicative discourse tree having a threshold number ofsimilarities with the communicative discourse tree; identifying whetherthe additional communicative discourse tree is from a positive set ofcommunicative discourse trees or a negative set of communicativediscourse trees, wherein the positive set of communicative discoursetrees comprises communicative discourse trees representing textcontaining a request for an explanation and the negative set ofcommunicative discourse trees comprises communicative discourse treesrepresenting text without a request for an explanation; and determining,based on the identifying, whether the text contains a request for anexplanation.
 6. The non-transitory computer-readable storage medium ofclaim 1, wherein when executed by the processing device, thecomputer-executable program instructions further cause the processingdevice to perform operations comprising: receiving utterances from auser device; and extracting the text from one or more of the utterances.7. The non-transitory computer-readable storage medium of claim 1,wherein the classification model is trained by iteratively: providing,to the classification model, a training communicative discourse tree;receiving, from the classification model, a classification strength of arequest for explanation; calculating a loss function by calculating adifference between an expected strength of a request for an explanationand the classification strength; and adjusting internal parameters ofthe classification model to minimize the loss function.
 8. A system forproviding a response to a user utterance in which a request forexplanation is detected, the system comprising: a non-transitorycomputer-readable storage medium storing computer-executable programinstructions; and at least one processing device communicatively coupledto the non-transitory computer-readable storage medium and executing thecomputer-executable program instructions comprising: accessing atraining set of communicative discourse trees, wherein a communicativediscourse tree is formed by creating a discourse tree from text, whereinthe discourse tree comprises a plurality of nodes, each nonterminal noderepresenting a rhetorical relationship between two fragments of text andeach terminal node of the nodes of the discourse tree is associated witha fragment of text, and wherein forming a communicative discourse treecomprises, in the discourse tree, matching each fragment that has a verbto a verb signature; for each communicative discourse tree of thetraining set of communicative discourse trees, iteratively training aclassification model by: providing the communicative discourse tree tothe classification model; receiving, from the classification model, aclassification that indicates either (i) a request for explanation or(ii) an absence of a request for an explanation; comparing theclassification to an expected classification; and adjusting internalparameters of the classification model based on the comparison; forming,from a user utterance received from a device, an additionalcommunicative discourse tree; identifying whether user utterancerepresents a request for an explanation by applying a classificationmodel to the additional communicative discourse tree; and preparing aresponse based on the identified request for explanation; and providingthe response to the device.
 9. The system of claim 8, wherein applyingthe classification model to the additional communicative discourse treecomprises determining one or more similarities between the additionalcommunicative discourse tree and a communicative discourse tree from thetraining set of communicative discourse trees.
 10. The system of claim8, wherein matching each fragment comprises: accessing verb signatures,wherein each verb signature comprises a verb of the respective fragmentand a sequence of thematic roles, wherein each thematic role describes arelationship between the verb and related words; determining, for eachverb signature of the verb signatures, a thematic role of the verbsignature that matches a role of a word in a respective fragment;selecting a particular verb signature from verb signatures based on theparticular verb signature comprising a highest number of matches; andassociating the particular verb signature with the respective fragment.11. The system of claim 8, wherein the training set of communicativediscourse trees comprises a positive set of communicative discoursetrees associated with text containing a request for explanation and anegative set of communicative discourse trees is associated with textnot containing a request for explanation, and wherein executing thecomputer-executable program instructions further configures theprocessing device to perform operations comprising adding the additionalcommunicative discourse tree to either the positive set of communicativediscourse trees or the negative set of communicative discourse treesbased on the identifying.
 12. The system of claim 8, wherein (i) theclassification model is a support vector machine with tree kernellearning or (ii) the classification model uses nearest neighbor learningof maximal common sub-trees.
 13. A system for detecting a request forexplanation and providing a response, the system comprising: anon-transitory computer-readable medium storing computer-executableprogram instructions; and at least one processing device communicativelycoupled to the non-transitory computer-readable medium, whereinexecuting the computer-executable program instructions configures theprocessing device to perform operations comprising: accessing text;creating a discourse tree from the text by identifying elementarydiscourse units in the text, wherein the discourse tree comprises aplurality of nodes, each nonterminal node representing a rhetoricalrelationship between two of the elementary discourse units and eachterminal node of the nodes of the discourse tree is associated with oneof the elementary discourse units; forming a communicative discoursetree that represents the text by matching each elementary discourse unitthat has a verb to a verb signature; identifying that the text comprisesa request for an explanation by applying a classification model trainedto detect a request for an explanation to the communicative discoursetree; preparing a response based on the identified request forexplanation; and providing the response to a user device.
 14. The systemof claim 13, wherein matching each elementary discourse unit comprises:accessing verb signatures, wherein each verb signature comprises theverb of the respective fragment and a sequence of thematic roles,wherein each thematic role describes a relationship between the verb andrelated words; determining, for each verb signature of the verbsignatures, a thematic role of the verb signature that matches a role ofa word in a respective fragment; selecting a particular verb signaturefrom the verb signatures based on the particular verb signaturecomprising a highest number of matches; and associating the particularverb signature with the respective fragment.
 15. The system of claim 14,wherein associating the particular verb signature with the respectivefragment further comprises: identifying each of thematic roles in theparticular verb signature; and matching, for each of the thematic rolesin the particular verb signature, a corresponding word in the respectivefragment to the thematic role.
 16. The system of claim 13, wherein (i)the classification model is a support vector machine with tree kernellearning or (ii) the classification model uses nearest neighbor learningof maximal common sub-trees.
 17. The system of claim 13, whereinapplying the classification model further comprises: selecting anadditional communicative discourse tree having a threshold number ofsimilarities with the communicative discourse tree; identifying whetherthe additional communicative discourse tree is from a positive set ofcommunicative discourse trees or a negative set of communicativediscourse trees, wherein the positive set of communicative discoursetrees is associated with text containing a request for explanation andthe negative set of communicative discourse trees is associated withtext not containing a request for explanation; and outputting whetherthe text contains a request for explanation based on the identifying.18. The system of claim 17, wherein executing the program instructionsfurther configures the processing device to perform operationscomprising: adding the additional communicative discourse tree to eitherthe positive set of communicative discourse trees or the negative set ofcommunicative discourse trees based on the identifying.
 19. The systemof claim 13, wherein executing the computer-executable programinstructions further configures the processing device to performoperations comprising: presenting, to the user device, textual contentcomprising a topic; receiving, from the user device and in response tothe presenting, a user utterance; forming an additional communicativediscourse tree that represents the user utterance; and identifying thatthe user utterance comprises a request for an explanation by applyingthe classification model to the additional communicative discourse tree.